Instructions to use BioMistral/BioMistral-7B-DARE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BioMistral/BioMistral-7B-DARE with Transformers:

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

pipe = pipeline("text-generation", model="BioMistral/BioMistral-7B-DARE")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BioMistral/BioMistral-7B-DARE")
model = AutoModelForCausalLM.from_pretrained("BioMistral/BioMistral-7B-DARE")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use BioMistral/BioMistral-7B-DARE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BioMistral/BioMistral-7B-DARE"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BioMistral/BioMistral-7B-DARE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BioMistral/BioMistral-7B-DARE

SGLang

How to use BioMistral/BioMistral-7B-DARE 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 "BioMistral/BioMistral-7B-DARE" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BioMistral/BioMistral-7B-DARE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "BioMistral/BioMistral-7B-DARE" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BioMistral/BioMistral-7B-DARE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BioMistral/BioMistral-7B-DARE with Docker Model Runner:
```
docker model run hf.co/BioMistral/BioMistral-7B-DARE
```

harmful suggestions for ME/CFS disease

by pickler123 - opened Oct 5, 2025

Discussion

pickler123

Oct 5, 2025

Hello,

While evaluating this model for ME/CFS treatment It printed medically inaccurate and potentially dangerous advice regarding ME/CFS (Myalgic Encephalomyelitis).

It recommended exercise therapy and cognitive behavioural therapy as treatments, which directly contradicts current clinical guidelines stating that graded exercise can cause severe deterioration in patients (possibly death).

I’m not technically inclined to help in training directly. It may be ideal to post this issue here instead.

Model used: Mistral 7B Dare Q3_K_M

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