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---
license: apache-2.0
tags:
- pretrained
- mistral
- DNA
- codon
---
# Model Card for Mistral-Codon-v1-117M (Mistral for coding DNA)
The Mistral-Codon-v1-117M Large Language Model (LLM) is a pretrained generative DNA sequence model with 117M parameters.
It is derived from Mixtral-8x7B-v0.1 model, which was simplified for DNA: the number of layers and the hidden size were reduced.
The model was pretrained using 24M coding DNA sequences (300bp) from many different species (vertebrates, plants, bacteria, viruses, ...).
## 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-Codon-v1-117M", trust_remote_code=True)
model = AutoModel.from_pretrained("RaphaelMourad/Mistral-Codon-v1-117M", trust_remote_code=True)
```
## Calculate the embedding of a coding sequence
```
insulin = "TGA TGA TTG GCG CGG CTA GGA TCG GCT"
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-Codon-v1-117M is a pretrained base model for coding DNA.
## Contact
Raphaël Mourad. [email protected] |