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--- |
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license: apache-2.0 |
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datasets: |
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- pico-lm/pretokenized-dolma |
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language: |
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- en |
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metrics: |
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- pico-lm/perplexity |
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pipeline_tag: text-generation |
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--- |
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# Pico Decoder Tiny |
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**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. |
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## 🔧 Model Details |
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| Field | Value | |
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|---------------------|------------------------------------| |
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| **Architecture** | Decoder-only transformer (LLaMA-style) | |
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| **Parameters** | 11M | |
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| **Layers** | 12 | |
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| **Hidden Size** | 96 | |
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| **Feed Foward Size** | 384 | |
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| **Attention Heads** | 12 | |
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| **Key/Value Heads** | 4 | |
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## 📚 Training |
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- **Dataset**: [`pretokenized-dolma`](https://huggingface.co/datasets/pico-lm/pretokenized-dolma), English-only |
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- **Training steps**: 200,000 |
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- **Batch size**: 1024 |
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- **Sequence length**: 2048 |
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- **Optimizer**: AdamW |
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- **Learning rate schedule**: Linear decay with warmup |
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- **Compute**: 16 A100-SXM4-80GB GPUs |
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## 📈 Evaluation and Analysis |
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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. |
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We also evaluate perplexity of the model on the [`pico-paloma-tinsy`](https://huggingface.co/datasets/pico-lm/pretokenized-paloma-tinsy) dataset. |
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## 📄 Citation |
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If you use `pico-tiny` or any other `pico-decoder` model in your research, please cite: |
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```bibtex |
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@software{pico2025, |
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author = {Diehl Martinez, Richard}, |
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title = {Pico: A Lightweight Framework for Studying Language Model Learning Dynamics}, |
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year = {2025, |
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url = {https://github.com/pico-lm} |
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} |
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``` |
