Pico Decoder Large
pico-decoder-large is the largest model (570M) in the current pico-decoder suite. It is a full-scale research model designed for in-depth interpretability studies of transformer learning. Trained with pico-train and fully compatible with pico-analyze, it offers rich checkpointing and analytical insight into large-scale LM behavior.
NOTE: The
pico-decoder-large-1branch contains the full commit history for the training run.
🔧 Model Details
| Field | Value |
|---|---|
| Architecture | Decoder-only transformer (LLaMA-style) |
| Parameters | 570M |
| Layers | 12 |
| Hidden Size | 1536 |
| Feed Forward Size | 6144 |
| 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}
}