README.md

metadata

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

Model Card for Mistral-Chem-v1-134M (Mistral for chemistry)

The Mistral-Chem-v1-134M Large Language Model (LLM) is a pretrained generative chemical molecule model with 134M parameters. It is derived from Mixtral-8x7B-v0.1 model, which was simplified for molecules: the number of layers and the hidden size were reduced. The model was pretrained using 10M molecule SMILES strings from the ZINC 15 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-Chem-v1-134M", trust_remote_code=True) 
model = AutoModel.from_pretrained("RaphaelMourad/Mistral-Chem-v1-134M", trust_remote_code=True)

Calculate the embedding of a DNA sequence

chem = "CCCCC[C@H](Br)CC"
inputs = tokenizer(chem, 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-Chem-v1-134M is a pretrained base model for chemistry.

Contact

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