[whatmodel.sh](../../README.md) / MedGemma 4B IT
# MedGemma 4B IT
Google · released 2025-05 · 4.3B params · health-ai-developer-foundations
**score 47.3** ±2.4 · #424 of 732 · from 4 benchmark results across 4 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks MedGemma 4B IT among every model on that task or test, worst to best.
other tests (4)
test result vs the others in its run difficulty counts why
Polish medical board exams, bias-… 43.4% ▄·▂▂●··█▂▄·▄ beats 4/13 hard · avg 46% 36% at its level
BRIDGE clinical text, zero-shot a… 29.4% ▁▁▁▁▁▂▂●▄▆█▃ beats 29/97 hard · avg 31% 33% blunt · at its level
Open PT-LLM Leaderboard average (… 68.2% ▃▁▁▁▁▁▁▁▂●▅▁ beats 149/276 medium · avg 72% 15% at its level · noisy
SurgiQ surgical MCQ (zero-shot, l… 44.1% ▂···▂▂·●▂▃██ beats 4/17 medium · avg 53% 13% at its level
source results share of the score
Reassessing High-Performing LLMs on Pol… 1 36% ▇▇▇▇
BRIDGE Medical Leaderboard 1 33% ▇▇▇▇
Open Portuguese LLM Leaderboard 1 15% ▇▇
SurgiQ: A Large-Scale Multi-Domain Benc… 1 13% ▇▇
prior: pull toward the middle 3% ▇
weighted average = 47.3
Licences: 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/)) · BRIDGE Medical Leaderboard ([MIT](../../licenses/bridge-medical-leaderboard-MIT.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · 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 MedGemma 4B IT (Q4_K_M, needs ~2.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/medgemma-4b-it-GGUF/tree/6b5f10216d3daf032d97307a92a27bac1ccb8355))
```sh
llama cli -hf lmstudio-community/medgemma-4b-it-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/medgemma-4b-it-GGUF:Q4_K_M
```
Other versions: Q8_0 (~4.1 GB) `llama cli -hf lmstudio-community/medgemma-4b-it-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 MedGemma 4B IT (Q4_K_M, needs ~2.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/medgemma-4b-it-GGUF/tree/6b5f10216d3daf032d97307a92a27bac1ccb8355))
```sh
lms get https://huggingface.co/lmstudio-community/medgemma-4b-it-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 (~4.1 GB) `lms get https://huggingface.co/lmstudio-community/medgemma-4b-it-GGUF@Q8_0 -y` · 4bit (~3 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/medgemma-4b-it-MLX-4bit -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 MedGemma 4B IT (Q4_K_M, needs ~3.3 GB, from [ollama.com](https://ollama.com/library/medgemma/tags))
```sh
ollama run medgemma:4b
```
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run MedGemma 4B IT (4bit, needs ~3 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/medgemma-4b-it-MLX-4bit/tree/9f902e33954f60880103bf512688fa5edc6bd9ea))
```sh
mlx_lm.chat --model lmstudio-community/medgemma-4b-it-MLX-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model lmstudio-community/medgemma-4b-it-MLX-4bit
```
Model repository and documentation: [huggingface.co/google](https://huggingface.co/google/medgemma-4b-it)
[interactive version](https://whatmodel.sh/model/medgemma-4b-it) · [JSON](https://whatmodel.sh/api?format=json&all=1)