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--- |
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license: apache-2.0 |
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tags: |
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- pretrained |
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- mistral |
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- peptide |
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--- |
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# Model Card for Mistral-Peptide-v1-422M (Mistral for peptide) |
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The Mistral-Peptide-v1-422M Large Language Model (LLM) is a pretrained generative peptide molecule model with 422M parameters. |
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It is derived from Mixtral-8x7B-v0.1 model, which was simplified for protein: the number of layers and the hidden size were reduced. |
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The model was pretrained using 863499 peptide strings. |
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## Model Architecture |
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Like Mixtral-8x7B-v0.1, it is a transformer model, with the following architecture choices: |
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- Grouped-Query Attention |
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- Sliding-Window Attention |
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- Byte-fallback BPE tokenizer |
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- Mixture of Experts |
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## Load the model from huggingface: |
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``` |
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import torch |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained("RaphaelMourad/Mistral-Peptide-v1-422M", trust_remote_code=True) |
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model = AutoModel.from_pretrained("RaphaelMourad/Mistral-Peptide-v1-422M", trust_remote_code=True) |
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``` |
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## Calculate the embedding of a protein sequence |
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``` |
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insulin = "MALWMRLLPLLALLALWG" |
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inputs = tokenizer(insulin, return_tensors = 'pt')["input_ids"] |
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hidden_states = model(inputs)[0] # [1, sequence_length, 256] |
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# embedding with max pooling |
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embedding_max = torch.max(hidden_states[0], dim=0)[0] |
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print(embedding_max.shape) # expect to be 256 |
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``` |
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## Troubleshooting |
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Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer. |
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## Notice |
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Mistral-Peptide-v1-422M is a pretrained base model for peptide. |
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## Contact |
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Raphaël Mourad. [email protected] |
