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--- |
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language: protein |
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tags: |
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- protein |
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datasets: |
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- uniref-100 |
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--- |
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# RITA-M |
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RITA is a family of autoregressive protein models, developed by a collaboration of [Lighton](https://lighton.ai/), the [OATML group](https://oatml.cs.ox.ac.uk/) at Oxford, and the [Debbie Marks Lab](https://www.deboramarkslab.com/) at Harvard. |
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Model | #Params | d_model | layers | lm loss uniref-100 |
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--- | --- | --- | --- | --- | |
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[Small](https://huggingface.co/lightonai/RITA_s) | 85M | 768 | 12 | 2.31 |
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[**Medium**](https://huggingface.co/lightonai/RITA_m) | 300M | 1024 | 24 | 2.01 |
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[Large](https://huggingface.co/lightonai/RITA_l)| 680M | 1536 | 24 | 1.82 |
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[XLarge](https://huggingface.co/lightonai/RITA_xl)| 1.2B | 2048 | 24 | 1.70 |
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For full results see our preprint: https://arxiv.org/abs/2205.05789 |
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## Usage |
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Instantiate a model like so: |
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``` python |
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from transformers import AutoModel, AutoModelForCausalLM |
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model = AutoModelForCausalLM.from_pretrained("lightonai/RITA_m, trust_remote_code=True") |
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tokenizer = AutoTokenizer.from_pretrained("lightonai/RITA_m") |
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``` |
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for generation we support pipelines: |
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``` python |
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from transformers import pipeline |
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rita_gen = pipeline('text-generation', model=model, tokenizer=tokenizer) |
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sequences = rita_gen("MAB", max_length=20, do_sample=True, top_k=950, repetition_penalty=1.2, |
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num_return_sequences=2, eos_token_id=2) |
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for seq in sequences: |
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print(f"seq: {seq['generated_text'].replace(' ', '')}") |
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``` |
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## How to cite |
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@misc{RITA2022, |
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doi = {10.48550/ARXIV.2205.05789}, |
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url = {https://arxiv.org/abs/2205.05789}, |
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author = {Hesslow, Daniel and Zanichelli, Niccoló and Notin, Pascal and Poli, Iacopo and Marks, Debora}, |
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title = {RITA: a Study on Scaling Up Generative Protein Sequence Models}, |
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publisher = {arXiv}, |
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year = {2022}, |
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} |
