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+ ---
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+ license: apache-2.0
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+ datasets:
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+ - pico-lm/pretokenized-paloma
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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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+
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+ # Pico Decoder Large
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+
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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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+
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+ ## ๐Ÿ”ง Model Details
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+
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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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+
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+ ## ๐Ÿ“š Training
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+
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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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+
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+ ## ๐Ÿ“ˆ Evaluation and Analysis
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+
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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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+
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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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+
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+ ## ๐Ÿ“„ Citation
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+
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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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+ }