[whatmodel.sh](../../README.md) / BioMistral-7B
# BioMistral-7B
BioMistral · released 2024-02 · apache-2.0
**score 39.3** ±3.0 · #574 of 732 · from 3 benchmark results across 3 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks BioMistral-7B among every model on that task or test, worst to best.
other tests (3)
test result vs the others in its run difficulty counts why
BRIDGE clinical text, zero-shot a… 20.4% ▁▁▁▁▁●▂▃▄▆█▃ beats 12/97 hard · avg 31% 42% blunt
Polish medical board exams, bias-… 30.6% ●·▂▂▂··█▂▄·▄ beats 1/13 hard · avg 46% 38% typical
SurgiQ surgical MCQ (zero-shot, l… 47.1% ▂···▂▂·▃●▃██ beats 5/17 medium · avg 53% 16% typical
source results share of the score
BRIDGE Medical Leaderboard 1 42% ▇▇▇▇▇
Reassessing High-Performing LLMs on Pol… 1 38% ▇▇▇▇▇
SurgiQ: A Large-Scale Multi-Domain Benc… 1 16% ▇▇
prior: pull toward the middle 4% ▇
weighted average = 39.3
Licences: BRIDGE Medical Leaderboard ([MIT](../../licenses/bridge-medical-leaderboard-MIT.txt)) · Reassessing High-Performing LLMs on Polish Medical Exams: True Competence or Bias-Driven Performance? ([CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)) · SurgiQ: A Large-Scale Multi-Domain Benchmark for Evaluating Surgical Understanding in Large Language Models ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run BioMistral-7B (Q4_K_M, needs ~4.4 GB, from [huggingface.co/BioMistral](https://huggingface.co/BioMistral/BioMistral-7B-GGUF/tree/de8c2dfcead24fd23ccb33f6ca5ff015e9ecdb4b))
```sh
llama cli -hf BioMistral/BioMistral-7B-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf BioMistral/BioMistral-7B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.7 GB) `llama cli -hf BioMistral/BioMistral-7B-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run BioMistral-7B (Q4_K_M, needs ~4.4 GB, from [huggingface.co/BioMistral](https://huggingface.co/BioMistral/BioMistral-7B-GGUF/tree/de8c2dfcead24fd23ccb33f6ca5ff015e9ecdb4b))
```sh
lms get https://huggingface.co/BioMistral/BioMistral-7B-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~7.7 GB) `lms get https://huggingface.co/BioMistral/BioMistral-7B-GGUF@Q8_0 -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run BioMistral-7B (Q4_K_M, needs ~4.4 GB, from [huggingface.co/BioMistral](https://huggingface.co/BioMistral/BioMistral-7B-GGUF/tree/de8c2dfcead24fd23ccb33f6ca5ff015e9ecdb4b))
```sh
ollama run hf.co/BioMistral/BioMistral-7B-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.7 GB) `ollama run hf.co/BioMistral/BioMistral-7B-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (BF16, needs ~14.48 GB, from [huggingface.co/BioMistral](https://huggingface.co/BioMistral/BioMistral-7B/tree/9a11e1ffa817c211cbb52ee1fb312dc6b61b40a5))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='BioMistral/BioMistral-7B', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/BioMistral](https://huggingface.co/BioMistral/BioMistral-7B)
[interactive version](https://whatmodel.sh/model/biomistral-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)