Pico Decoder Tiny
pico-decoder-tiny is the smallest (11M) model in the pico-decoder suite — a lightweight, LLaMA-style decoder-only transformer trained from scratch using pico-train. It is designed for transparent and reproducible research into the learning dynamics of language models, and is fully compatible with the pico-analyze toolkit for detailed interpretability analysis.
NOTE: The
pico-decoder-tiny-1branch contains the full commit history for the training run.
🔧 Model Details
| Field | Value |
|---|---|
| Architecture | Decoder-only transformer (LLaMA-style) |
| Parameters | 11M |
| Layers | 12 |
| Hidden Size | 96 |
| Feed Foward Size | 384 |
| Attention Heads | 12 |
| Key/Value Heads | 4 |
📚 Training
- Dataset:
pretokenized-dolma, English-only - 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
If you use pico-tiny or any other pico-decoder model in your research, please cite:
@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}
}