🧠
medgemma-4b-it model by google
⭐ 5.6
🧠

medgemma-4b-it

by google Model ID: hf-model--google--medgemma-4b-it
FNI 5.6
Top 70%
🔗 View Source
Audited 5.6 FNI Score
4.3B Params
4k Context
Hot 589.2K Downloads
8G GPU ~5GB Est. VRAM

⚡ Quick Commands

đŸĻ™ Ollama Run
ollama run medgemma-4b-it
🤗 HF Download
huggingface-cli download google/medgemma-4b-it
đŸ“Ļ Install Lib
pip install -U transformers
📊

Engineering Specs

⚡ Hardware

Parameters
4.3B
Architecture
Gemma3ForConditionalGeneration
Context Length
4K
Model Size
12.7GB

🧠 Lifecycle

Library
-
Precision
float16
Tokenizer
-

🌐 Identity

Source
HuggingFace
License
Open Access
💾

Est. VRAM Benchmark

~4.5GB

Analyze Hardware

* Technical estimation for FP16/Q4 weights. Does not include OS overhead or long-context batching. For Technical Reference Only.

🕸ī¸ Neural Mesh Hub

Interconnecting Research, Data & Ecosystem

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🔗 Core Ecosystem

đŸ”Ŧ

đŸ”Ŧ Research & Data

📈 Interest Trend

--

* Real-time activity index across HuggingFace, GitHub and Research citations.

🔍 Semantic Keywords

🏷ī¸ transformers 🏷ī¸ safetensors 🏷ī¸ gemma3 🏷ī¸ any-to-any 🏷ī¸ medical 🏷ī¸ radiology 🏷ī¸ clinical-reasoning 🏷ī¸ dermatology 🏷ī¸ pathology 🏷ī¸ ophthalmology 🏷ī¸ chest-x-ray 🏷ī¸ image-text-to-text 🏷ī¸ conversational 🏷ī¸ arxiv:2303.15343 🏷ī¸ arxiv:2507.05201 🏷ī¸ arxiv:2405.03162 🏷ī¸ arxiv:2106.14463 🏷ī¸ arxiv:2412.03555 🏷ī¸ arxiv:2501.19393 🏷ī¸ arxiv:2009.13081 🏷ī¸ arxiv:2102.09542 🏷ī¸ arxiv:2411.15640 🏷ī¸ arxiv:2404.05590 🏷ī¸ arxiv:2501.18362 🏷ī¸ base_model:google/medgemma-4b-pt 🏷ī¸ base_model:finetune:google/medgemma-4b-pt 🏷ī¸ license:other 🏷ī¸ text-generation-inference 🏷ī¸ endpoints_compatible 🏷ī¸ region:us

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đŸ”ŦTechnical Deep Dive

Full Specifications [+]
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🚀 What's Next?

📊

Find Training Datasets

Discover datasets compatible with this model

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Compare Benchmarks

See how this model ranks on standard tests

⚡

Deployment Guide

Understand deployment options

⚡ Quick Commands

đŸĻ™ Ollama Run
ollama run medgemma-4b-it
🤗 HF Download
huggingface-cli download google/medgemma-4b-it
đŸ“Ļ Install Lib
pip install -U transformers
đŸ–Ĩī¸

Hardware Compatibility

Multi-Tier Validation Matrix

Live Sync
🎮 Compatible

RTX 3060 / 4060 Ti

Entry 8GB VRAM
🎮 Compatible

RTX 4070 Super

Mid 12GB VRAM
đŸ’ģ Compatible

RTX 4080 / Mac M3

High 16GB VRAM
🚀 Compatible

RTX 3090 / 4090

Pro 24GB VRAM
🏗ī¸ Compatible

RTX 6000 Ada

Workstation 48GB VRAM
🏭 Compatible

A100 / H100

Datacenter 80GB VRAM
ℹī¸

Pro Tip: Compatibility is estimated for 4-bit quantization (Q4). High-precision (FP16) or ultra-long context windows will significantly increase VRAM requirements.

README

Neural Fact Sheet: medgemma-4b-it

[!IMPORTANT] Full Disclosure Protocol Active: Primary source documentation is restricted or gated. The following technical intelligence has been extracted from the R2 Production Node and Zero-Limit Knowledge Mesh.

📊 Core Architecture

  • Parameter Scale: 4.3B
  • Neural Architecture: Gemma3ForConditionalGeneration
  • Inference Efficiency: 5.6/100 (FNI Logic Score)
  • License Profile: Proprietary/Restricted

⚙ī¸ Technical Capabilities

  • Neural Context Window: 4k tokens
  • Memory Footprint: ~5GB (Q4) estimated VRAM
  • Pipeline Origin: Standard AI
  • Safety Status: Model utilizes developer-defined safety filters.

🚀 Strategic Recommendations

  1. Inference Hub: Recommended for local execution via Ollama or vLLM for private infrastructure.
  2. Context Limits: Optimal performance is maintained within the first 4k tokens of input.
  3. Hardware Alignment: Ideal for hardware with at least ~5GB (Q4) of high-speed video memory.

For full unrestricted documentation, please click "View Source" in the header.

ZEN MODE â€ĸ README

⚠ī¸ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

View Original Source →

📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.
  • â€ĸ Source: Unknown
📜

Cite this model

Academic & Research Attribution

BibTeX 
@misc{hf_model__google__medgemma_4b_it,
  author = {google},
  title = {undefined Model},
  year = {2026},
  howpublished = {\url{https://huggingface.co/google/medgemma-4b-it}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
google. (2026). undefined [Model]. Free2AITools. https://huggingface.co/google/medgemma-4b-it
🤗 Data Source: Hugging Face ↗
🔄 Daily sync (03:00 UTC)

AI Summary: Based on Hugging Face metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseℹī¸ Verify with original source

🛡ī¸ Model Transparency Report

Verified data manifest for traceability and transparency.

100% Data Disclosure Active

🆔 Identity & Source

id
hf-model--google--medgemma-4b-it
author
google
tags
transformerssafetensorsgemma3any-to-anymedicalradiologyclinical-reasoningdermatologypathologyophthalmologychest-x-rayimage-text-to-textconversationalarxiv:2303.15343arxiv:2507.05201arxiv:2405.03162arxiv:2106.14463arxiv:2412.03555arxiv:2501.19393arxiv:2009.13081arxiv:2102.09542arxiv:2411.15640arxiv:2404.05590arxiv:2501.18362base_model:google/medgemma-4b-ptbase_model:finetune:google/medgemma-4b-ptlicense:othertext-generation-inferenceendpoints_compatibleregion:us

⚙ī¸ Technical Specs

architecture
Gemma3ForConditionalGeneration
params billions
4.3
context length
4,096
vram gb
4.5
vram is estimated
true
vram formula
VRAM ≈ (params * 0.75) + 0.8GB (KV) + 0.5GB (OS)

📊 Engagement & Metrics

likes
786
downloads
589,155

Free2AITools Constitutional Data Pipeline: Curated disclosure mode active. (V15.x Standard)