| --- |
| library_name: transformers |
| tags: |
| - biology |
| - protein-language-model |
| - protein-generation |
| - causal-lm |
| - mixture-of-experts |
| - transformers |
| --- |
| |
| # Model Card for ProtGPT3-112M |
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| ## Model Description |
| ProtGPT3-112M is a single-sequence autoregressive protein language model for protein sequence generation. It is the smallest model in the [ProtGPT3 family](https://huggingface.co/collections/AI4PD/protgpt3-family), an open-source suite of promptable and aligned protein language models ranging from 112M to 10B parameters. ProtGPT3 models use a causal Mixtral-style Mixture-of-Experts architecture and are trained for causal language modeling on protein sequences. |
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| For more info and guidance on how to generate sequences with ProtGPT3-112M check out the extensive description provided in [ProtGPT3-1.3B](https://huggingface.co/AI4PD/ProtGPT3-1.3B), just replacing the model name (i.e., `model_name=AI4PD/ProtGPT3-112M`). |
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| Also consider using the [ProtGPT3-112M-dpo](https://huggingface.co/AI4PD/ProtGPT3-112M-dpo) version for an equivalent model size, but with improved sequence generation. |
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| ### Out-of-Scope Use |
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| The model should not be used as the sole basis for experimental, clinical, environmental, or safety-critical decisions. Generated proteins require downstream computational and experimental validation. The model is not guaranteed to generate functional, soluble, safe, or synthesizable proteins. |
|
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| ## Bias, Risks, and Limitations |
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| ProtGPT3-112M learns from public protein sequence datasets and may reproduce biases present in those datasets. Generated sequences may be low-complexity, nonfunctional, unstable, insoluble, or biologically implausible. Protein generation models may also present dual-use risks if used irresponsibly. |
|
|
| ## Citation |
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|
| **BibTeX:** |
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|
| ```bibtex |
| @article{garibbo2026protgpt3, |
| title={ProtGPT3: an Open-source family of Promptable and Aligned Protein Language Models}, |
| author={Garibbo, Michele and Boxo Corominas, Gerard and Stocco, Filippo and Illanes Vicioso, Ramiro and Middendorf, Lasse and Ferruz, Noelia}, |
| journal={bioRxiv}, |
| pages={2026--06}, |
| year={2026}, |
| publisher={Cold Spring Harbor Laboratory} |
| } |
| ``` |
|
|
| ## More Information |
| For guidance on how to generate sequences with ProtGPT3-112M check out the extensive description provided in [ProtGPT3-1.3B](https://huggingface.co/AI4PD/ProtGPT3-1.3B). |
| All models and code are released through the Hugging Face ecosystem and accompanying code repository. |
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|
