Instructions to use ig1/BioMistral-7B-FP8-Dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ig1/BioMistral-7B-FP8-Dynamic with Transformers:

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

pipe = pipeline("text-generation", model="ig1/BioMistral-7B-FP8-Dynamic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ig1/BioMistral-7B-FP8-Dynamic")
model = AutoModelForCausalLM.from_pretrained("ig1/BioMistral-7B-FP8-Dynamic")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ig1/BioMistral-7B-FP8-Dynamic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ig1/BioMistral-7B-FP8-Dynamic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ig1/BioMistral-7B-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ig1/BioMistral-7B-FP8-Dynamic

SGLang

How to use ig1/BioMistral-7B-FP8-Dynamic 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 "ig1/BioMistral-7B-FP8-Dynamic" \
    --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": "ig1/BioMistral-7B-FP8-Dynamic",
		"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 "ig1/BioMistral-7B-FP8-Dynamic" \
        --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": "ig1/BioMistral-7B-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ig1/BioMistral-7B-FP8-Dynamic with Docker Model Runner:
```
docker model run hf.co/ig1/BioMistral-7B-FP8-Dynamic
```

BioMistral-7B-FP8-Dynamic

Overview

BioMistral-7B-FP8-Dynamic is an FP8 Dynamic–quantized version of the BioMistral-7B model, designed for high-performance inference while maintaining strong quality on biomedical and medical NLP tasks.

This model is primarily intended for deployment with vLLM on modern GPUs (Hopper / Ada architectures).

Base Model

Base model: BioMistral-7B
Architecture: Mistral-style decoder-only Transformer
Domain: Biomedical / Medical Natural Language Processing

Quantization

Method: FP8 Dynamic
Scope: Linear layers
Objective: Reduce VRAM usage and improve inference throughput

Notes

The weights are already quantized.
Do not apply additional runtime quantization.

Intended Use

Biomedical and medical text generation
Medical writing assistance
Summarization and analysis of scientific literature
Medical RAG pipelines (clinical notes, research papers)

ig1
/

BioMistral-7B-FP8-Dynamic