metadata

license: apache-2.0
datasets:
  - pico-lm/pretokenized-dolma
language:
  - en
metrics:
  - pico-lm/perplexity
pipeline_tag: text-generation

Pico Decoder Medium

pico-decoder-medium is a 181M parameter model in the pico-decoder suite, balancing scale and analyzability. Built with pico-train and instrumented with pico-analyze, it enables detailed studies of layer-wise learning behavior during language model pretraining.

NOTE: The pico-decoder-medium-1 branch contains the full commit history for the training run.

🔧 Model Details

Field	Value
Architecture	Decoder-only transformer (LLaMA-style)
Parameters	181M
Layers	12
Hidden Size	768
Feed Forward Size	3072
Attention Heads	12
Key/Value Heads	4

📚 Training

Dataset: pretokenized-dolma
Training steps: 200,000
Batch size: 1024
Sequence length: 2048
Optimizer: AdamW
Learning rate schedule: Linear decay with warmup
Compute: 16 A100-SXM4-80GB GPUs

📈 Evaluation and Analysis

This model supports fine-grained analysis using pico-analyze. This tool enables researchers to understand how learning unfolds over training, even at very small scales.

We also evaluate perplexity of the model on the pico-paloma-tinsy dataset.

📄 Citation

@software{pico2025,
    author = {Diehl Martinez, Richard},
    title = {Pico: A Lightweight Framework for Studying Language Model Learning Dynamics},
    year = {2025},
    url = {https://github.com/pico-lm}
}