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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 Large |
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**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`](https://github.com/pico-lm) and fully compatible with [`pico-analyze`](https://github.com/pico-lm), it offers rich checkpointing and analytical insight into large-scale LM behavior. |
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> NOTE: The `pico-decoder-large-1` branch contains the full commit history for the training run. |
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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** | 570M | |
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| **Layers** | 12 | |
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| **Hidden Size** | 1536 | |
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| **Feed Forward Size**| 6144 | |
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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://github.com/pico-lm) |
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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). 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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```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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