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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` and fully compatible with `pico-analyze`, it offers rich checkpointing and analytical insight into large-scale LM behavior. This model supports fine-grained analysis using pico-analyze. This tool enables researchers to understand how learning unfolds over training, even at very small scales. ```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} }

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**pico-decoder-medium** is a 181M parameter model in the `pico-decoder` suite, balancing scale and analyzability. Built with `pico-train` and instrumented with `pico-analyze`, it enables detailed studies of layer-wise learning behavior during language model pretraining. This model supports fine-grained analysis using pico-analyze. This tool enables researchers to understand how learning unfolds over training, even at very small scales. ```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} }

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**pico-decoder-small** is a 65M parameter model in the `pico-decoder` suite — a lightweight, LLaMA-style decoder-only transformer trained from scratch using `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. This model supports fine-grained analysis using `pico-analyze`. This tool enables researchers to understand how learning unfolds over training, even at modest scales. ```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} }

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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`. 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. This model supports fine-grained analysis using `pico-analyze`. This tool enables researchers to understand how learning unfolds over training, even at very small scales.

Text generation
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Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.

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