---
license: apache-2.0
tags:
- pretrained
- mistral
- DNA
- biology
- genomics
---

# Model Card for mixtral-dna-yeast-v0.2 (mistral for DNA)

The mixtral-dna-yeast-v0.2 Large Language Model (LLM) is a pretrained generative DNA text model with 17.31M parameters x 8 experts = 138.5M parameters. 
It is derived from Mistral-7B-v0.1 model, which was simplified for DNA: the number of layers and the hidden size were reduced. 
The model was pretrained using around 1000 yeast genomes with 10kb DNA sequences. 

The yeast genomes are from: https://www.nature.com/articles/s41586-018-0030-5

For full details of this model please read our [github repo](https://github.com/raphaelmourad/Mistral-DNA).

## 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/mixtral-dna-yeast-v0.2", trust_remote_code=True) # Same as DNABERT2
model = AutoModel.from_pretrained("RaphaelMourad/mixtral-dna-yeast-v0.2", trust_remote_code=True)
```

## Calculate the embedding of a DNA sequence

```
dna = "TGATGATTGGCGCGGCTAGGATCGGCT"
inputs = tokenizer(dna, 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-DNA is a pretrained base model for DNA.

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