Instructions to use RaphaelMourad/Mistral-DNA-v1-138M-bacteria with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RaphaelMourad/Mistral-DNA-v1-138M-bacteria with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RaphaelMourad/Mistral-DNA-v1-138M-bacteria")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RaphaelMourad/Mistral-DNA-v1-138M-bacteria")
model = AutoModelForCausalLM.from_pretrained("RaphaelMourad/Mistral-DNA-v1-138M-bacteria")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RaphaelMourad/Mistral-DNA-v1-138M-bacteria with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RaphaelMourad/Mistral-DNA-v1-138M-bacteria"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RaphaelMourad/Mistral-DNA-v1-138M-bacteria",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RaphaelMourad/Mistral-DNA-v1-138M-bacteria

SGLang

How to use RaphaelMourad/Mistral-DNA-v1-138M-bacteria with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RaphaelMourad/Mistral-DNA-v1-138M-bacteria" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RaphaelMourad/Mistral-DNA-v1-138M-bacteria",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "RaphaelMourad/Mistral-DNA-v1-138M-bacteria" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RaphaelMourad/Mistral-DNA-v1-138M-bacteria",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RaphaelMourad/Mistral-DNA-v1-138M-bacteria with Docker Model Runner:
```
docker model run hf.co/RaphaelMourad/Mistral-DNA-v1-138M-bacteria
```

Model Card for Mistral-DNA-v1-138M-bacteria (mistral for DNA)

The Mistral-DNA-v1-138M-bacteria 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 700 bacterial genomes with 10kb DNA sequences.

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-DNA-v1-138M-bacteria", trust_remote_code=True) # Same as DNABERT2
model = AutoModel.from_pretrained("RaphaelMourad/Mistral-DNA-v1-138M-bacteria", 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-v1-138M-bacteria is a pretrained base model for DNA.

Contact

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

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Safetensors

Model size

0.1B params

Tensor type

BF16