Model Card for Mistral-chem-v0.4 (mistral for chemistry)
The Mistral-chem-v0.4 Large Language Model (LLM) is a pretrained generative chemical molecule model with 52.11M parameters x 8 experts = 416.9M parameters. It is derived from Mistral-7B-v0.1 model, which was simplified for chemistry: the number of layers and the hidden size were reduced. The model was pretrained using 45M molecule SMILES strings from the Zinc database.
For full details of this model please read our github repo.
Model Architecture
Like Mistral-7B-v0.1, it is a transformer model, with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
Load the model from huggingface:
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("RaphaelMourad/Mistral-chem-v0.4", trust_remote_code=True)
model = AutoModel.from_pretrained("RaphaelMourad/Mistral-chem-v0.4", 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 is a pretrained base model for chemistry.
Contact
Raphaël Mourad. raphael.mourad@univ-tlse3.fr
- Downloads last month
- 23