whatmodel.sh / Biomni-R0-32B-Preview

Biomni-R0-32B-Preview

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Biomni-R0-32B-Preview is an open-weights language model from Biomni (Stanford), released October 2025. Its published size is 32.76B parameters. Licence: mit.

Catalogue identity notes

Maker repo biomni/Biomni-R0-32B-Preview @71432eb: licence tag mit. 32,762,123,264 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2025-10: month the maker's Hugging Face repo was created (createdAt 2025-10-06).

53.3±4.4#360 of 949 LLMsfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Biomni-R0-32B-Preview, out of 1132 with a score.

#	model	score	where	RAM	released
468	GLM-4 9B Chat 1M Zhipu AI (THUDM)	53±5	local	~8.1GB	2024-06
469	Hulu-Med-14B ZJU AI4H	53±4	comparison only		2025-10
470	Biomni-R0-32B-Preview Biomni (Stanford)	53±4	local	~22GB	2025-10
471	Mistral Small 3 24B (2501) Mistral	53±1	local	~16GB	2025-01
472	Qwen2-VL 72B Instruct Alibaba	53±1	local	~34GB	2024-09

see all LLMs · find the ones that fit your machine

$ what it's good at

other tests1 not in a task · 91% of its score

test	result	vs the others in its run	difficulty	counts	why
BRIDGE clinical text, zero-shot average (87 tasks)	35.3% ✎	▁▁▁▁▁▂▂▃▄▆█▃ beats 48/97	hard · avg 32%	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 91%	blunt · at its level

$ where to run it

llama.cpp · recommended

version

Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
Windows
irm https://llama.app/install.ps1 | iex
other ways to install ↗
Run Biomni-R0-32B-Preview · needs ~19.8 GB · from huggingface.co/mradermacher
chat in the terminal
llama cli -hf mradermacher/Biomni-R0-32B-Preview-GGUF:Q4_K_M
Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf mradermacher/Biomni-R0-32B-Preview-GGUF:Q4_K_M

LM Studio

version

Install LM Studio · Open the app once before using the lms command.
download it (macOS / Windows / Linux) ↗
Run Biomni-R0-32B-Preview · needs ~19.8 GB · from huggingface.co/mradermacher
open in LM Studio ↗
Or download it in the terminal
lms get https://huggingface.co/mradermacher/Biomni-R0-32B-Preview-GGUF@Q4_K_M -y
Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

Install Ollama
download it (macOS / Windows) ↗
Linux
curl -fsSL https://ollama.com/install.sh | sh
Run Biomni-R0-32B-Preview · needs ~19.8 GB · from huggingface.co/mradermacher
chat in the terminal
ollama run hf.co/mradermacher/Biomni-R0-32B-Preview-GGUF:Q4_K_M

Other ways to run it

Run it as published · needs ~131.05 GB · from huggingface.co/biomni (official)
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='biomni/Biomni-R0-32B-Preview', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"

Official weights and documentation: huggingface.co/biomni ↗

$ how the score was worked out · BRIDGE Medical Leaderboard (H… 91%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

source	results	share of the score	licence
BRIDGE Medical Leaderboard (Harvard Medical School / Brigham and Women's YLab)	1	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 91%	MIT
prior · pull toward the middle		▇▇▇ 9%
weighted average		= 53.3

$ improve this page

Update model information Add a place to run it Add a test result Propose a benchmark Correct a benchmark score Edit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated 2026-10-11 02:20 UTC · sources and licences · json