stablelm-4e1t-2b-v0.1

15
2.0B
license:cc-by-sa-4.0
by
pszemraj
Language Model
OTHER
2B params
New
15 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
5GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
2GB+ RAM

Training Data Analysis

🟡 Average (5.3/10)

Researched training datasets used by stablelm-4e1t-2b-v0.1 with quality assessment

Specialized For

code
general
science
multilingual

Training Datasets (2)

the pile
🟢 8/10
code
general
science
multilingual
Key Strengths
  • •Deliberate Diversity: Explicitly curated to include diverse content types (academia, code, Q&A, book...
  • •Documented Quality: Each component dataset is thoroughly documented with rationale for inclusion, en...
  • •Epoch Weighting: Component datasets receive different training epochs based on perceived quality, al...
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...

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

configyaml
base_model: pszemraj/stablelm-3b-4e1t-prune10
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

strict: false
seed: 80085

# dataset
datasets:
    - path: BEE-spoke-data/KI-smorgasbord_fw-small
      type: completion # format from earlier
      field: text # Optional[str] default: text, field to use for completion data
val_set_size: 0.015

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: false
train_on_inputs: false
group_by_length: false

# WANDB
wandb_project: llama3-pruning
wandb_entity: pszemraj
wandb_watch: gradients
wandb_name: stablelm-4e1t-2b-v0.1
hub_model_id: pszemraj/stablelm-4e1t-2b-v0.1
hub_strategy: every_save

gradient_accumulation_steps: 16
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_torch_fused # paged_adamw_32bit
weight_decay: 0.05
lr_scheduler: cosine
learning_rate: 5e-5
warmup_ratio: 0.1

load_in_8bit: false
load_in_4bit: false
bf16: true
tf32: true

flash_attention: true
torch_compile: true # requires >= torch 2.0, may sometimes cause problems
torch_compile_backend: inductor # Optional[str]
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

# hyperparams for freq of evals, saving, etc
evals_per_epoch: 5
saves_per_epoch: 3
save_safetensors: true
save_total_limit: 1
output_dir: ./output-axolotl/output-model-2b
logging_steps: 8

deepspeed:

special_tokens:
  pad_token: <|end_of_text|>

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