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---
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
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