metadata

license: apache-2.0
tags:
  - pretrained
  - mistral
  - protein

Model Card for Mistral-Prot-v1-417M (Mistral for protein)

The Mistral-Prot-v1-417M Large Language Model (LLM) is a pretrained generative protein molecule model with 417M parameters. It is derived from Mixtral-8x7B-v0.1 model, which was simplified for protein: the number of layers and the hidden size were reduced. The model was pretrained using 10M protein strings from the uniprot 50 database.

Model Architecture

Like Mixtral-8x7B-v0.1, it is a transformer model, with the following architecture choices:

Grouped-Query Attention
Sliding-Window Attention
Byte-fallback BPE tokenizer
Mixture of Experts

Load the model from huggingface:

import torch
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("RaphaelMourad/Mistral-Prot-v1-417M", trust_remote_code=True) 
model = AutoModel.from_pretrained("RaphaelMourad/Mistral-Prot-v1-417M", trust_remote_code=True)

Calculate the embedding of a protein sequence

insulin = "MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPLALEGSLQKRGIVEQCCTSICSLYQLENYCN"
inputs = tokenizer(insulin, return_tensors = 'pt')["input_ids"]
hidden_states = model(inputs)[0] # [1, sequence_length, 256]

# embedding with max pooling
embedding_max = torch.max(hidden_states[0], dim=0)[0]
print(embedding_max.shape) # expect to be 256

Troubleshooting

Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer.

Notice

Mistral-Prot-v1-417M is a pretrained base model for protein.

Contact

Raphaël Mourad. raphael.mourad@univ-tlse3.fr