Model Details
Full Model IDgoogle/medgemma-27b-it
Pipeline / Taskimage-text-to-text
Librarytransformers
Downloads (all-time)610.8K
Likes370
Last Modified7/10/2025
Author / Orggoogle
PrivateNo — public
⚡ Quick Usage (Python)
Using the 🤗 Transformers library. Install with pip install transformers
from transformers import pipeline
# Load the model
pipe = pipeline("image-text-to-text", model="google/medgemma-27b-it")
# Run inference
result = pipe("Your input here")
print(result)🏷️ Tags
transformerssafetensorsmedicalx-raypathologydermatologyfundusradiology report generationchest-x-raymedical-embeddingsimage-classificationzero-shot-image-classificationimage-feature-extractionimage-text-to-textenarxiv:2303.15343arxiv:2507.05201arxiv:2405.03162arxiv:2106.14463arxiv:2412.03555arxiv:2501.19393arxiv:2009.13081arxiv:2102.09542arxiv:2411.15640arxiv:2404.05590arxiv:2501.18362base_model:google/gemma-3-27b-ptbase_model:finetune:google/gemma-3-27b-ptlicense:otherendpoints_compatible
More image-text-to-text Models
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Access model files, inference API, and full documentation on Hugging Face.
Open on Hugging Face →Browse Model Files ↗← Browse All Models🤖 Task: image-text-to-text
This model is designed for the image-text-to-text task. Explore more models for this use case.
All image-text-to-text Models →📊 Popularity
⬇ Downloads610.8K
❤️ Community Likes370
🛠️ Requirements
- →Install: pip install transformers
- →Python 3.8+ recommended for Transformers.
- →GPU (CUDA) speeds up inference significantly.
- →Use model.half() for fp16 on limited VRAM.