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

# 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`](https://github.com/pico-lm/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-1` branch 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`](https://huggingface.co/datasets/pico-lm/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`](https://github.com/pico-lm/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`](https://huggingface.co/datasets/pico-lm/pretokenized-paloma-tinsy) dataset.

## 📄 Citation

If you use `pico-tiny` or any other `pico-decoder` model in your research, please cite:

```bibtex
@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}
}
```