Llama-3.1-8B-tldr

21
2
8.0B
llama
by
RedHatAI
Language Model
OTHER
8B params
New
21 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
18GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
8GB+ RAM

Training Data Analysis

🟡 Average (4.8/10)

Researched training datasets used by Llama-3.1-8B-tldr with quality assessment

Specialized For

general
science
multilingual
reasoning

Training Datasets (4)

common crawl
🔴 2.5/10
general
science
Key Strengths
  • •Scale and Accessibility: At 9.5+ petabytes, Common Crawl provides unprecedented scale for training d...
  • •Diversity: The dataset captures billions of web pages across multiple domains and content types, ena...
  • •Comprehensive Coverage: Despite limitations, Common Crawl attempts to represent the broader web acro...
Considerations
  • •Biased Coverage: The crawling process prioritizes frequently linked domains, making content from dig...
  • •Large-Scale Problematic Content: Contains significant amounts of hate speech, pornography, violent c...
c4
🔵 6/10
general
multilingual
Key Strengths
  • •Scale and Accessibility: 750GB of publicly available, filtered text
  • •Systematic Filtering: Documented heuristics enable reproducibility
  • •Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • •English-Only: Limits multilingual applications
  • •Filtering Limitations: Offensive content and low-quality text remain despite filtering
wikipedia
🟡 5/10
science
multilingual
Key Strengths
  • •High-Quality Content: Wikipedia articles are subject to community review, fact-checking, and citatio...
  • •Multilingual Coverage: Available in 300+ languages, enabling training of models that understand and ...
  • •Structured Knowledge: Articles follow consistent formatting with clear sections, allowing models to ...
Considerations
  • •Language Inequality: Low-resource language editions have significantly lower quality, fewer articles...
  • •Biased Coverage: Reflects biases in contributor demographics; topics related to Western culture and ...
arxiv
🟡 5.5/10
science
reasoning
Key Strengths
  • •Scientific Authority: Peer-reviewed content from established repository
  • •Domain-Specific: Specialized vocabulary and concepts
  • •Mathematical Content: Includes complex equations and notation
Considerations
  • •Specialized: Primarily technical and mathematical content
  • •English-Heavy: Predominantly English-language papers

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true
Trainingyaml
base_model: meta-llama/Llama-3.1-8B

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: trl-lib/tldr
    type:
      system_prompt: "Give a TL;DR of the following Reddit post."
      field_system: system
      field_instruction: prompt
      field_output: completion
      format: "<|user|>\n{instruction}\n<|assistant|>\n"
      no_input_format: "<|user|>\n{instruction}\n<|assistant|>\n"
    split: train

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: true

torch.compile: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 1e-5
max_grad_norm: 1

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

train_on_inputs: false
bf16: auto
fp16:
tf32: false

early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
val_set_size: 0.05
save_strategy: "best"
save_total_limit: 1
metric_for_best_model: "loss"

debug:
deepspeed:
weight_decay: 0.0
special_tokens:
  pad_token: "<|end_of_text|>"

seed: 0

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true

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