| --- |
| datasets: |
| - pico-lm/pretokenized-dolma |
| language: |
| - en |
| license: apache-2.0 |
| 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} |
| } |
| ``` |
