Tess-3-7B-SFT

5
4
license:apache-2.0
by
migtissera
Language Model
OTHER
7B params
New
5 downloads
Early-stage
Edge AI:
Mobile
Laptop
Server
16GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
7GB+ RAM

Code Examples

yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
Sample code to run inferencepythontransformers
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")

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