medgemma-4b-it-GGUF Open-source Medical Multimodal Model - Process Images and Texts for Radiology and Other Scenarios

Medgemma 4b It GGUF

Developed by second-state

medgemma-4b-it is a multimodal model focused on the medical field, capable of processing image and text inputs, and suitable for multiple medical scenarios such as radiology and clinical reasoning.

Text-to-Image

Transformers

Open Source License:Other #Medical multimodality #Radiology analysis #Clinical reasoning

Downloads 564

Release Time : 5/22/2025

Model Overview

This model is a medical - specific model developed by Google, supporting multimodal processing of images and text, and specifically optimized for medical image analysis and clinical text understanding.

Model Features

Optimized for the medical field

Specifically optimized for data and tasks in the medical field, including radiology, dermatology, pathology, etc.

Multimodal processing

Capable of simultaneously processing image and text inputs, suitable for medical image analysis and clinical text understanding.

Efficient quantization

Provides multiple quantization versions, from 2 - bit to 16 - bit, to meet the deployment requirements under different hardware conditions.

Model Capabilities

Medical image analysis

Clinical text understanding

Multimodal reasoning

Radiology diagnosis assistance

Dermatology analysis

Pathology analysis

Ophthalmology analysis

Chest X - ray interpretation

Use Cases

Medical diagnosis assistance

Radiology image analysis

Analyze medical images such as X - rays and CT scans and provide diagnostic suggestions.

Clinical report generation

Generate clinical reports based on medical images and patient data.

Medical research

Medical literature analysis

Help researchers quickly understand and analyze a large amount of medical literature.

🚀 medgemma-4b-it-GGUF

This project focuses on the quantized model medgemma-4b-it-GGUF, which is based on the original Google model. It supports image - text - to - text tasks and is suitable for various medical fields.

🚀 Quick Start

Prerequisites

LlamaEdge version: v0.18.5 and above

Prompt Template

Prompt type: gemma-3
Prompt string

<bos><start_of_turn>user
{user_message}<end_of_turn>
<start_of_turn>model
{model_message}<end_of_turn>model

Context Size

128000

Run as LlamaEdge Service

Chat

wasmedge --dir .:. --nn-preload default:GGML:AUTO:medgemma-4b-it-Q5_K_M.gguf \
  llama-api-server.wasm \
  --prompt-template gemma-3 \
  --ctx-size 128000 \
  --model-name medgemma-4b

Images

⚠️ Important Note

Input images are required to be normalized to 896 x 896 resolution and encoded to 256 tokens each.

wasmedge --dir .:. --nn-preload default:GGML:AUTO:medgemma-4b-it-Q5_K_M.gguf \
  llama-api-server.wasm \
  --prompt-template gemma-3 \
  --llava-mmproj medgemma-4b-it-mmproj-f16.gguf \
  --ctx-size 128000 \
  --model-name medgemma-4b

Run as LlamaEdge Command App

wasmedge --dir .:. \
  --nn-preload default:GGML:AUTO:medgemma-4b-it-Q5_K_M.gguf \
  llama-chat.wasm \
  --prompt-template gemma-3 \
  --ctx-size 128000

✨ Features

Medical Application: Suitable for multiple medical fields such as radiology, clinical reasoning, dermatology, pathology, ophthalmology, and chest - x - ray.
Quantized Models: Provides a variety of quantized models to meet different storage and performance requirements.

📦 Installation

This README does not provide specific installation steps. Please refer to the official documentation of LlamaEdge for installation guidance.

📚 Documentation

Original Model

google/medgemma-4b-it

Quantized GGUF Models

Property	Details
Model Type	Quantized versions of `medgemma-4b-it`
Training Data	Not provided

Name	Quant method	Bits	Size	Use case
medgemma-4b-it-Q2_K.gguf	Q2_K	2	1.73 GB	smallest, significant quality loss - not recommended for most purposes
medgemma-4b-it-Q3_K_L.gguf	Q3_K_L	3	2.24 GB	small, substantial quality loss
medgemma-4b-it-Q3_K_M.gguf	Q3_K_M	3	2.10 GB	very small, high quality loss
medgemma-4b-it-Q3_K_S.gguf	Q3_K_S	3	1.94 GB	very small, high quality loss
medgemma-4b-it-Q4_0.gguf	Q4_0	4	2.36 GB	legacy; small, very high quality loss - prefer using Q3_K_M
medgemma-4b-it-Q4_K_M.gguf	Q4_K_M	4	2.49 GB	medium, balanced quality - recommended
medgemma-4b-it-Q4_K_S.gguf	Q4_K_S	4	2.38 GB	small, greater quality loss
medgemma-4b-it-Q5_0.gguf	Q5_0	5	2.76 GB	legacy; medium, balanced quality - prefer using Q4_K_M
medgemma-4b-it-Q5_K_M.gguf	Q5_K_M	5	2.83 GB	large, very low quality loss - recommended
medgemma-4b-it-Q5_K_S.gguf	Q5_K_S	5	2.76 GB	large, low quality loss - recommended
medgemma-4b-it-Q6_K.gguf	Q6_K	6	3.19 GB	very large, extremely low quality loss
medgemma-4b-it-Q8_0.gguf	Q8_0	8	4.13 GB	very large, extremely low quality loss - not recommended
medgemma-4b-it-f16.gguf	f16	16	7.77 GB
medgemma-4b-it-mmproj-f16.gguf	f16	16	851 MB

Quantized with llama.cpp b5452

📄 License

License name: health - ai - developer - foundations
License link: https://developers.google.com/health-ai-developer-foundations/terms

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