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Model: Bercraft/medgemma-1.5-4b-it-GGUF Source: Original Platform
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---
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license: other
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license_name: health-ai-developer-foundations
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license_link: https://developers.google.com/health-ai-developer-foundations/terms
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library_name: transformers
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pipeline_tag: image-text-to-text
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extra_gated_heading: Access MedGemma on Hugging Face
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extra_gated_prompt: >-
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To access MedGemma on Hugging Face, you're required to review and
|
||||
agree to [Health AI Developer Foundation's terms of use](https://developers.google.com/health-ai-developer-foundations/terms).
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To do this, please ensure you're logged in to Hugging Face and click below.
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Requests are processed immediately.
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extra_gated_button_content: Acknowledge license
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tags:
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- medical
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- radiology
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- clinical-reasoning
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- dermatology
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- pathology
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- ophthalmology
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- chest-x-ray
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---
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# MedGemma 1.5 model card
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Note: This card describes MedGemma 1.5, which is only available as a 4B
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multimodal instruction-tuned variant. For information on MedGemma 1 variants,
|
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refer to the [MedGemma 1 model
|
||||
card](https://developers.google.com/health-ai-developer-foundations/medgemma/model-card-v1).
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**Model documentation:** [MedGemma](https://developers.google.com/health-ai-developer-foundations/medgemma)
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|
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**Resources:**
|
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|
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* Model on Google Cloud Model Garden: [MedGemma](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/medgemma)
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* Models on Hugging Face: [Collection](https://huggingface.co/collections/google/medgemma-release-680aade845f90bec6a3f60c4)
|
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* Concept applications built using MedGemma: [Collection](https://huggingface.co/collections/google/medgemma-concept-apps-686ea036adb6d51416b0928a)
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* [GitHub repository](https://github.com/google-health/medgemma)
|
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* [Tutorial notebooks](https://github.com/google-health/medgemma/blob/main/notebooks)
|
||||
|
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* License: The use of MedGemma is governed by the [Health AI Developer
|
||||
Foundations terms of
|
||||
use](https://developers.google.com/health-ai-developer-foundations/terms).
|
||||
MedGemma has not been evaluated or optimized for multi-turn applications.
|
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|
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MedGemma's training may make it more sensitive to the specific prompt used than
|
||||
Gemma 3.
|
||||
|
||||
When adapting MedGemma developer should consider the following:
|
||||
|
||||
|
||||
|
||||
* License: The use of MedGemma is governed by the [Health AI Developer
|
||||
Foundations terms of
|
||||
use](https://developers.google.com/health-ai-developer-foundations/terms).
|
||||
|
||||
* [Support](https://developers.google.com/health-ai-developer-foundations/medgemma/get-started.md#contact)
|
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channels
|
||||
|
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**Author:** Google
|
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|
||||
## Model information
|
||||
|
||||
This section describes the specifications and recommended use of the MedGemma
|
||||
model.
|
||||
|
||||
### Description
|
||||
|
||||
MedGemma is a collection of [Gemma 3](https://ai.google.dev/gemma/docs/core)
|
||||
variants that are trained for performance on medical text and image
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||||
comprehension. Developers can use MedGemma to accelerate building
|
||||
healthcare-based AI applications.
|
||||
|
||||
MedGemma 1.5 4B is an updated version of the MedGemma 1 4B model.
|
||||
|
||||
MedGemma 1.5 4B expands support for several new medical imaging and data
|
||||
processing applications, including:
|
||||
|
||||
* **High-dimensional medical imaging:** Interpretation of three-dimensional
|
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volume representations of Computed Tomography (CT) and Magnetic Resonance
|
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Imaging (MRI).
|
||||
* **Whole-slide histopathology imaging (WSI):** Simultaneous interpretation of
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multiple patches from a whole slide histopathology image as input.
|
||||
* **Longitudinal medical imaging:** Interpretation of chest X-rays in the
|
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context of prior images (e.g., comparing current versus historical scans).
|
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* **Anatomical localization:** Bounding box–based localization of anatomical
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features and findings in chest X-rays.
|
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* **Medical document understanding:** Extraction of structured data, such as
|
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values and units, from unstructured medical lab reports.
|
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* **Electronic Health Record (EHR) understanding:** Interpretation of
|
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text-based EHR data.
|
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|
||||
In addition to these new features, MedGemma 1.5 4B delivers improved accuracy on
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medical text reasoning and modest improvement on standard 2D image
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interpretation compared to MedGemma 1 4B.
|
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|
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MedGemma utilizes a [SigLIP](https://arxiv.org/abs/2303.15343) image encoder
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that has been specifically pre-trained on a variety of de-identified medical
|
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data, including chest X-rays, dermatology images, ophthalmology images, and
|
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histopathology slides. The LLM component is trained on a diverse set of medical
|
||||
data, including medical text, medical question-answer pairs, FHIR-based
|
||||
electronic health record data, 2D and 3D radiology images, histopathology
|
||||
images, ophthalmology images, dermatology images, and lab reports for document
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understanding.
|
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|
||||
MedGemma 1.5 4B has been evaluated on a range of clinically relevant benchmarks
|
||||
to illustrate its baseline performance. These evaluations are based on both open
|
||||
benchmark datasets and internally curated datasets. Developers are expected to
|
||||
fine-tune MedGemma for improved performance on their use case. Consult the
|
||||
[Intended use section](https://developers.google.com/health-ai-developer-foundations/medgemma/model-card.md#intended_use)
|
||||
for more details.
|
||||
|
||||
MedGemma is optimized for medical applications that involve a text generation
|
||||
component. For medical image-based applications that do not involve text
|
||||
generation, such as data-efficient classification, zero-shot classification, or
|
||||
content-based or semantic image retrieval, the [MedSigLIP image
|
||||
encoder](https://developers.google.com/health-ai-developer-foundations/medsiglip/model-card)
|
||||
is recommended. MedSigLIP is based on the same image encoder that powers
|
||||
MedGemma 1 and MedGemma 1.5.
|
||||
|
||||
### How to use
|
||||
|
||||
The following are some example code snippets to help you quickly get started
|
||||
running the model locally on GPU.
|
||||
|
||||
Note: If you need to use the model at scale, we recommend creating a production
|
||||
version using [Model
|
||||
Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/medgemma).
|
||||
Model Garden provides various deployment options and tutorial notebooks,
|
||||
including specialized server-side image processing options for efficiently
|
||||
handling large medical images: Whole Slide Digital Pathology (WSI) or volumetric
|
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scans (CT/MRI) stored in [Cloud DICOM
|
||||
Store](https://docs.cloud.google.com/healthcare-api/docs/concepts/dicom) or
|
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[Google Cloud Storage (GCS)](https://cloud.google.com/storage).
|
||||
|
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First, install the Transformers library. Gemma 3 is supported starting from
|
||||
transformers 4.50.0.
|
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|
||||
```sh
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$ pip install -U transformers
|
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```
|
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|
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Next, use either the pipeline wrapper or the transformer API directly to send a
|
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chest X-ray image and a question to the model.
|
||||
|
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Note that CT, MRI and whole-slide histopathology images require some
|
||||
pre-processing; see the
|
||||
[CT](https://github.com/google-health/medgemma/blob/main/notebooks/high_dimensional_ct_hugging_face.ipynb)
|
||||
and
|
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[WSI](https://github.com/google-health/medgemma/blob/main/notebooks/high_dimensional_pathology_hugging_face.ipynb)
|
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notebook for examples.
|
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|
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**Run model with the pipeline API**
|
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|
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```python
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from transformers import pipeline
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from PIL import Image
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import requests
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import torch
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pipe = pipeline(
|
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"image-text-to-text",
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model="google/medgemma-1.5-4b-it",
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torch_dtype=torch.bfloat16,
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device="cuda",
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)
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|
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# Image attribution: Stillwaterising, CC0, via Wikimedia Commons
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image_url = "https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png"
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image = Image.open(requests.get(image_url, headers={"User-Agent": "example"}, stream=True).raw)
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image", "image": image},
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{"type": "text", "text": "Describe this X-ray"}
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]
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}
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]
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output = pipe(text=messages, max_new_tokens=2000)
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print(output[0]["generated_text"][-1]["content"])
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```
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|
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**Run the model directly**
|
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|
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```python
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# Make sure to install the accelerate library first via `pip install accelerate`
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from transformers import AutoProcessor, AutoModelForImageTextToText
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from PIL import Image
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import requests
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import torch
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|
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model_id = "google/medgemma-1.5-4b-it"
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|
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model = AutoModelForImageTextToText.from_pretrained(
|
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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processor = AutoProcessor.from_pretrained(model_id)
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|
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# Image attribution: Stillwaterising, CC0, via Wikimedia Commons
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image_url = "https://upload.wikimedia.org/wikipedia/commons/c/c8/Chest_Xray_PA_3-8-2010.png"
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image = Image.open(requests.get(image_url, headers={"User-Agent": "example"}, stream=True).raw)
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "image", "image": image},
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{"type": "text", "text": "Describe this X-ray"}
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]
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}
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]
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inputs = processor.apply_chat_template(
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messages, add_generation_prompt=True, tokenize=True,
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return_dict=True, return_tensors="pt"
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).to(model.device, dtype=torch.bfloat16)
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input_len = inputs["input_ids"].shape[-1]
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with torch.inference_mode():
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generation = model.generate(**inputs, max_new_tokens=2000, do_sample=False)
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generation = generation[0][input_len:]
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decoded = processor.decode(generation, skip_special_tokens=True)
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print(decoded)
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```
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|
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### Examples
|
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|
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Refer to the growing collection of [tutorial
|
||||
notebooks](https://github.com/google-health/medgemma/blob/main/notebooks) to see
|
||||
how to use or fine-tune MedGemma.
|
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|
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### Model architecture overview
|
||||
|
||||
The MedGemma model is built based on [Gemma 3](https://ai.google.dev/gemma/) and
|
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uses the same decoder-only transformer architecture as Gemma 3. To read more
|
||||
about the architecture, consult the Gemma 3 [model
|
||||
card](https://ai.google.dev/gemma/docs/core/model_card_3).
|
||||
|
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### Technical specifications
|
||||
|
||||
* **Model type**: Decoder-only Transformer architecture, see the [Gemma 3
|
||||
Technical
|
||||
Report](https://storage.googleapis.com/deepmind-media/gemma/Gemma3Report.pdf)
|
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* **Input modalities**: Text, vision (multimodal)
|
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* **Output modality**: Text only
|
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* **Attention mechanism**: Grouped-query attention (GQA)
|
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* **Context length**: Supports long context, at least 128K tokens
|
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* **Key publication**: [https://arxiv.org/abs/2604.05081](https://arxiv.org/abs/2604.05081)
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* **Model created**: **4B multimodal**: Jan 13, 2026
|
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* **Model version**: **4B multimodal**: 1.5.0
|
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|
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### Citation
|
||||
|
||||
When using this model, please cite:
|
||||
Sellergren et al. "MedGemma 1.5 Technical Report." *arXiv preprint arXiv:2604.05081* (2026).
|
||||
|
||||
```none
|
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@article{sellergren2026medgemma,
|
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title={MedGemma 1.5 Technical Report},
|
||||
author={Sellergren, Andrew and Gao, Chufan and Mahvar, Fereshteh and Kohlberger, Timo and Jamil, Fayaz and Traverse, Madeleine and Tono, Alberto and Sadjad, Bashir and Yang, Lin and Lau, Charles and others},
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journal={arXiv preprint arXiv:2604.05081},
|
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year={2026}
|
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}
|
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```
|
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|
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### Inputs and outputs
|
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|
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**Input**:
|
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|
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* Text string, such as a question or prompt
|
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* Images, normalized to 896 x 896 resolution and encoded to 256 tokens each
|
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* Total input length of 128K tokens
|
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|
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**Output**:
|
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|
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* Generated text in response to the input, such as an answer to a question,
|
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analysis of image content, or a summary of a document
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* Total output length of 8192 tokens
|
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|
||||
### Performance and evaluations
|
||||
|
||||
MedGemma was evaluated across a range of different multimodal classification,
|
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report generation, visual question answering, and text-based tasks.
|
||||
|
||||
### Key performance metrics
|
||||
|
||||
#### Imaging evaluations
|
||||
|
||||
The multimodal performance of MedGemma 1.5 4B was evaluated across a range of
|
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benchmarks, focusing on radiology (2D, longitudinal 2D, and 3D), dermatology,
|
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histopathology, ophthalmology, document understanding, and multimodal clinical
|
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reasoning. See Data card for details of individual datasets.
|
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|
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We also list the previous results for MedGemma 1 4B and 27B (multimodal models
|
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only), as well as for Gemma 3 4B for comparison.
|
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|
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| Task / Dataset | Metric | Gemma 3 4B | MedGemma 1 4B | MedGemma 1.5 4B | MedGemma 1 27B |
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| :---- | :---- | :---- | :---- | :---- | :---- |
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| **3D radiology image classification** | | | | | |
|
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| CT Dataset 1\*(7 conditions/abnormalities) | Macro accuracy | 54.5 | 58.2 | 61.1 | 57.8 |
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| CT-RATE (validation, 18 conditions/abnormalities ) | Macro F1 | | 23.5 | 27.0 | |
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| | Macro precision | | 34.5 | 34.2 | |
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| | Macro recall | | 34.1 | 42.0 | |
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| MRI Dataset 1\*(10 conditions/abnormalities) | Macro accuracy | 51.1 | 51.3 | 64.7 | 57.4 |
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| **2D image classification** | | | | | |
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| MIMIC CXR\*\* | Macro F1 (top 5 conditions) | 81.2 | 88.9 | 89.5 | 90.0 |
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| CheXpert CXR | Macro F1 (top 5 conditions) | 32.6 | 48.1 | 48.2 | 49.9 |
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| CXR14 | Macro F1 (3 conditions) | 32.0 | 50.1 | 48.4 | 45.3 |
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| PathMCQA\* (histopathology) | Accuracy | 37.1 | 69.8 | 70.0 | 71.6 |
|
||||
| WSI-Path\* (whole-slide histopathology) | ROUGE | 2.3 | 2.2 | 49.4 | 4.1 |
|
||||
| US-DermMCQA\* | Accuracy | 52.5 | 71.8 | 73.5 | 71.7 |
|
||||
| EyePACS\* (fundus) | Accuracy | 14.4 | 64.9 | 76.8 | 75.3 |
|
||||
| **Disease Progression Classification (Longitudinal)** | | | | | |
|
||||
| MS-CXR-T | Macro Accuracy | 59.0 | 61.11 | 65.7 | 50.1 |
|
||||
| **Visual question answering** | | | | | |
|
||||
| SLAKE (radiology) | Tokenized F1 | 40.2 | 72.3 | 59.7\*\*\*\* | 70.3 |
|
||||
| | Accuracy (on closed subset) | 62.0 | 87.6 | 82.8 | 85.9 |
|
||||
| VQA-RAD\*\*\* (radiology) | Tokenized F1 | 33.6 | 49.9 | 48.1 | 46.7 |
|
||||
| | Accuracy (on closed subset) | 42.1 | 69.1 | 70.2 | 67.1 |
|
||||
| **Region of interest detection** | | | | | |
|
||||
| Chest ImaGenome: Anatomy bounding box detection | Intersection over union | 5.7 | 3.1 | 38.0 | 16.0 |
|
||||
| **Multimodal medical knowledge and reasoning** | | | | | |
|
||||
| MedXpertQA (text \+ multimodal questions) | Accuracy | 16.4 | 18.8 | 20.9 | 26.8 |
|
||||
|
||||
\* Internal datasets. CT Dataset 1 and MRI Dataset 1 are described below \-- for
|
||||
evaluation, perfectly balanced samples were drawn per condition. US-DermMCQA is
|
||||
described in [Liu et al. (2020, Nature
|
||||
medicine)](https://www.nature.com/articles/s41591-020-0842-3), presented as a
|
||||
4-way MCQ per example for skin condition classification. PathMCQA is based on
|
||||
multiple datasets, presented as 3-9 way MCQ per example for identification,
|
||||
grading, and subtype for breast, cervical, and prostate cancer. WSI-Path is a
|
||||
dataset of deidentified H\&E WSIs and associated final diagnosis text from
|
||||
original pathology reports, comprising single WSI examples and previously
|
||||
described in [Ahmed et al. (2024, arXiv)](https://arxiv.org/pdf/2406.19578).
|
||||
EyePACS is a dataset of fundus images with classification labels based on
|
||||
5-level diabetic retinopathy severity (None, Mild, Moderate, Severe,
|
||||
Proliferative). A subset of these datasets are described in more detail in the
|
||||
[MedGemma 1.5 Technical Report](https://arxiv.org/abs/2604.05081).
|
||||
|
||||
\*\* Based on radiologist adjudicated labels, described in [Yang (2024,
|
||||
arXiv)](https://arxiv.org/pdf/2405.03162) Section A.1.1.
|
||||
|
||||
\*\*\* Based on "balanced split," described in [Yang (2024,
|
||||
arXiv)](https://arxiv.org/pdf/2405.03162).
|
||||
|
||||
\*\*\*\* While MedGemma 1.5 4B exhibits strong radiology interpretation
|
||||
capabilities, it was less optimized for the SLAKE Q\&A format compared to
|
||||
MedGemma 1 4B. Fine-tuning on SLAKE may improve results.
|
||||
|
||||
#### Chest X-ray report generation
|
||||
|
||||
MedGemma chest X-ray (CXR) report generation performance was evaluated on
|
||||
[MIMIC-CXR](https://physionet.org/content/mimic-cxr/2.1.0/) using the [RadGraph
|
||||
F1 metric](https://arxiv.org/abs/2106.14463). We compare MedGemma 1.5 4B against
|
||||
a fine-tuned version of MedGemma 1 4B, and the MedGemma 1 27B base model.
|
||||
|
||||
| Task / Dataset | Metric | MedGemma 1 4B (tuned for CXR) | MedGemma 1.5 4B | MedGemma 1 27B |
|
||||
| :---- | :---- | :---- | :---- | :---- |
|
||||
| **Chest X-ray report generation** | | | | |
|
||||
| MIMIC CXR \- RadGraph F1 | | 30.3 | 27.2 | 27.0 |
|
||||
|
||||
#### Text evaluations
|
||||
|
||||
MedGemma 1.5 4B was evaluated across a range of text-only benchmarks for medical
|
||||
knowledge and reasoning. Existing results for MedGemma 1 variants and Gemma 3
|
||||
are shown for comparison.
|
||||
|
||||
| Dataset | Gemma 3 4B | MedGemma 1 4B | MedGemma 1.5 4B | MedGemma 1 27B |
|
||||
| :---- | :---- | :---- | :---- | :---- |
|
||||
| MedQA (4-op) | 50.7 | 64.4 | 69.1 | 85.3 |
|
||||
| MedMCQA | 45.4 | 55.7 | 59.8 | 70.2 |
|
||||
| PubMedQA | 68.4 | 73.4 | 68.2 | 77.2 |
|
||||
| MMLU Med | 67.2 | 70.0 | 69.6 | 86.2 |
|
||||
| MedXpertQA (text only) | 11.6 | 14.2 | 16.4 | 23.7 |
|
||||
| AfriMed-QA (25 question test set) | 48.0 | 52.0 | 56.0 | 72.0 |
|
||||
|
||||
#### Medical record evaluations
|
||||
|
||||
EHR understanding and interpretation was evaluated for synthetic longitudinal
|
||||
text-based EHR data and real-world de-identified discharge summaries via
|
||||
question-answering benchmark datasets for MedGemma 1.5 4B, MedGemma 1 variants,
|
||||
and Gemma 3 4B.
|
||||
|
||||
| Dataset | Metric | Gemma 3 4B | MedGemma 1 4B | MedGemma 1.5 4B | MedGemma 1 27B |
|
||||
| :---- | :---- | :---- | :---- | :---- | :---- |
|
||||
| EHRQA\* | Accuracy | 70.9 | 67.6 | 89.6 | 90.5 |
|
||||
| EHRNoteQA | Accuracy | 78.0 | 79.4 | 80.4 | 90.7 |
|
||||
|
||||
\* Internal dataset
|
||||
|
||||
#### Document understanding evaluations
|
||||
|
||||
Evaluation of converting unstructured medical lab reports documents
|
||||
(PDFs/images) into structured JSON data.
|
||||
|
||||
| Task / Dataset | Metric | Gemma 3 4B | MedGemma 1 4B | MedGemma 1.5 4B | MedGemma 1 27B |
|
||||
| :---- | :---- | :---- | :---- | :---- | :---- |
|
||||
| **PDF-to-JSON Lab Test Data Conversion** | | | | | |
|
||||
| EHR Dataset 2\* (raw PDF to JSON) | Macro F1 (average over per document F1 scores) | 84.0 | 78.0 | 91.0 | 76.0 |
|
||||
| | Micro F1 (F1 across all extracted data fields) | 81.0 | 75.0 | 88.0 | 70.0 |
|
||||
| EHR Dataset 3\* (raw PDF to JSON) | Macro F1 | 61.0 | 50.0 | 71.0 | 66.0 |
|
||||
| | Micro F1 | 61.0 | 51.0 | 70.0 | 69.0 |
|
||||
| Mendeley Clinical Laboratory Test Reports (PNG image to JSON) | Macro F1 | 83.0 | 85.0 | 85.0 | 69.0 |
|
||||
| | Micro F1 | 78.0 | 81.0 | 83.0 | 68.0 |
|
||||
| EHR Dataset 4\* | Macro F1 | 41.0 | 25.0 | 64.0 | |
|
||||
| | Micro F1 | 41.0 | 33.0 | 67.0 | |
|
||||
|
||||
\* Internal datasets.
|
||||
|
||||
### Ethics and safety evaluation
|
||||
|
||||
#### Evaluation approach
|
||||
|
||||
Our evaluation methods include structured evaluations and internal red-teaming
|
||||
testing of relevant content policies. Red-teaming was conducted by a number of
|
||||
different teams, each with different goals and human evaluation metrics. These
|
||||
models were evaluated against a number of different categories relevant to
|
||||
ethics and safety, including:
|
||||
|
||||
* **Child safety**: Evaluation of text-to-text and image-to-text prompts
|
||||
covering child safety policies, including child sexual abuse and
|
||||
exploitation.
|
||||
* **Content safety**: Evaluation of text-to-text and image-to-text prompts
|
||||
covering safety policies, including harassment, violence and gore, and hate
|
||||
speech.
|
||||
* **Representational harms**: Evaluation of text-to-text and image-to-text
|
||||
prompts covering safety policies, including bias, stereotyping, and harmful
|
||||
associations or inaccuracies.
|
||||
* **General medical harms**: Evaluation of text-to-text and image-to-text
|
||||
prompts covering safety policies, including information quality and
|
||||
potentially harmful responses or inaccuracies.
|
||||
|
||||
In addition to development level evaluations, we conduct "assurance evaluations"
|
||||
which are our "arms-length" internal evaluations for responsibility governance
|
||||
decision making. They are conducted separately from the model development team
|
||||
and inform decision making about release. High-level findings are fed back to
|
||||
the model team but prompt sets are held out to prevent overfitting and preserve
|
||||
the results' ability to inform decision making. Notable assurance evaluation
|
||||
results are reported to our Responsibility & Safety Council as part of release
|
||||
review.
|
||||
|
||||
#### Evaluation results
|
||||
|
||||
For all areas of safety testing, we saw safe levels of performance across the
|
||||
categories of child safety, content safety, and representational harms compared
|
||||
to previous Gemma models. All testing was conducted without safety filters to
|
||||
evaluate the model capabilities and behaviors. For both text-to-text and
|
||||
image-to-text the model produced minimal policy violations. A limitation of our
|
||||
evaluations was that they included primarily English language prompts.
|
||||
|
||||
## Data card
|
||||
|
||||
### Dataset overview
|
||||
|
||||
#### Training
|
||||
|
||||
The base Gemma models are pre-trained on a large corpus of text and code data.
|
||||
MedGemma multimodal variants utilize a
|
||||
[SigLIP](https://arxiv.org/abs/2303.15343) image encoder that has been
|
||||
specifically pre-trained on a variety of de-identified medical data, including
|
||||
radiology images, histopathology images, ophthalmology images, and dermatology
|
||||
images. Their LLM component is trained on a diverse set of medical data,
|
||||
including medical text, medical question-answer pairs, FHIR-based electronic
|
||||
health record data (27B multimodal only), radiology images, histopathology
|
||||
patches, ophthalmology images, and dermatology images.
|
||||
|
||||
#### Evaluation
|
||||
|
||||
MedGemma models have been evaluated on a comprehensive set of clinically
|
||||
relevant benchmarks across multiple datasets, tasks and modalities. These
|
||||
benchmarks include both open and internal datasets.
|
||||
|
||||
#### Source
|
||||
|
||||
MedGemma utilizes a combination of public and private datasets.
|
||||
|
||||
This model was trained on diverse public datasets including MIMIC-CXR (chest
|
||||
X-rays and reports), ChestImaGenome: Set of bounding boxes linking image
|
||||
findings with anatomical regions for MIMIC-CXR SLAKE (multimodal medical images
|
||||
and questions), PAD-UFES-20 (skin lesion images and data), SCIN (dermatology
|
||||
images), TCGA (cancer genomics data), CAMELYON (lymph node histopathology
|
||||
images), PMC-OA (biomedical literature with images), and Mendeley Digital Knee
|
||||
X-Ray (knee X-rays).
|
||||
|
||||
Additionally, multiple diverse proprietary datasets were licensed and
|
||||
incorporated (described next).
|
||||
|
||||
### Data ownership and documentation
|
||||
|
||||
* [MIMIC-CXR](https://physionet.org/content/mimic-cxr/2.1.0/): MIT Laboratory
|
||||
for Computational Physiology and Beth Israel Deaconess Medical Center
|
||||
(BIDMC).
|
||||
* [MS-CXR-T](https://physionet.org/content/ms-cxr-t/1.0.0/): Microsoft
|
||||
Research Health Futures, Microsoft Research.
|
||||
* [ChestX-ray14](https://pmc.ncbi.nlm.nih.gov/articles/PMC6476887/): National
|
||||
Institutes of Health \- Clinical Center.
|
||||
* [SLAKE](https://www.med-vqa.com/slake/): The Hong Kong Polytechnic
|
||||
University (PolyU), with collaborators including West China Hospital of
|
||||
Sichuan University and Sichuan Academy of Medical Sciences / Sichuan
|
||||
Provincial People's Hospital.
|
||||
* [PAD-UFES-20](https://pmc.ncbi.nlm.nih.gov/articles/PMC7479321/): Federal
|
||||
University of Espírito Santo (UFES), Brazil, through its Dermatological and
|
||||
Surgical Assistance Program (PAD).
|
||||
* [SCIN](https://github.com/google-research-datasets/scin): A collaboration
|
||||
between Google Health and Stanford Medicine.
|
||||
* [TCGA](https://portal.gdc.cancer.gov/) (The Cancer Genome Atlas): A joint
|
||||
effort of National Cancer Institute and National Human Genome Research
|
||||
Institute. Data from TCGA are available via the Genomic Data Commons (GDC)
|
||||
* [CAMELYON](https://camelyon17.grand-challenge.org/Data/): The data was
|
||||
collected from Radboud University Medical Center and University Medical
|
||||
Center Utrecht in the Netherlands.
|
||||
* [PMC-OA (PubMed Central Open Access
|
||||
Subset)](https://catalog.data.gov/dataset/pubmed-central-open-access-subset-pmc-oa):
|
||||
Maintained by the National Library of Medicine (NLM) and National Center for
|
||||
Biotechnology Information (NCBI), which are part of the NIH.
|
||||
* [MedQA](https://arxiv.org/pdf/2009.13081): This dataset was created by a
|
||||
team of researchers led by Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung
|
||||
Weng, Hanyi Fang, and Peter Szolovits.
|
||||
* [MedMCQA](https://arxiv.org/abs/2203.14371): This dataset was created by
|
||||
Ankit Pal, Logesh Kumar Umapathi and Malaikannan Sankarasubbu from Saama AI
|
||||
Research, Chennai, India
|
||||
* [PubMedQA](https://arxiv.org/abs/1909.06146): This dataset was created by
|
||||
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W. Cohen, Xinghua Lu from
|
||||
the University of Pittsburg, Carnegie Mellon University and Google.
|
||||
* [LiveQA](https://trec.nist.gov/pubs/trec26/papers/Overview-QA.pdf): This
|
||||
dataset was created by Ben Abacha Asma, Eugene Agichtein Yuval Pinter and
|
||||
Dina Demner-Fushman from the U.S. National Library of Medicine, Emory
|
||||
University and Georgia Institute of Technology.
|
||||
* [Mendeley Digital Knee
|
||||
X-Ray](https://data.mendeley.com/datasets/t9ndx37v5h/1): This dataset is
|
||||
from Rani Channamma University, and is hosted on Mendeley Data.
|
||||
* [AfriMed-QA](https://afrimedqa.com/): This data was developed and led by
|
||||
multiple collaborating organizations and researchers include key
|
||||
contributors: Intron Health, SisonkeBiotik, BioRAMP, Georgia Institute of
|
||||
Technology, and MasakhaneNLP.
|
||||
* [VQA-RAD](https://www.nature.com/articles/sdata2018251): This dataset was
|
||||
created by a research team led by Jason J. Lau, Soumya Gayen, Asma Ben
|
||||
Abacha, and Dina Demner-Fushman and their affiliated institutions (the US
|
||||
National Library of Medicine and National Institutes of Health)
|
||||
* [Chest ImaGenome](https://physionet.org/content/chest-imagenome/1.0.0/): IBM
|
||||
Research.
|
||||
* [MedExpQA](https://www.sciencedirect.com/science/article/pii/S0933365724001805):
|
||||
This dataset was created by researchers at the HiTZ Center (Basque Center
|
||||
for Language Technology and Artificial Intelligence).
|
||||
* [MedXpertQA](https://huggingface.co/datasets/TsinghuaC3I/MedXpertQA): This
|
||||
dataset was developed by researchers at Tsinghua University (Beijing, China)
|
||||
and Shanghai Artificial Intelligence Laboratory (Shanghai, China).
|
||||
* [HealthSearchQA](https://huggingface.co/datasets/katielink/healthsearchqa):
|
||||
This dataset consists of consisting of 3,173 commonly searched consumer
|
||||
questions.
|
||||
* [ISIC](https://www.isic-archive.com/): International Skin Imaging
|
||||
Collaboration is a joint effort involving clinicians, researchers, and
|
||||
engineers from various institutions worldwide.
|
||||
* [Mendeley Clinical Laboratory Test
|
||||
Reports](https://data.mendeley.com/datasets/bygfmk4rx9/2): This dataset is
|
||||
hosted on Mendeley and includes 260 clinical laboratory test reports issued
|
||||
by 24 laboratories in Egypt.
|
||||
* [CT-RATE](https://huggingface.co/datasets/ibrahimhamamci/CT-RATE): Istanbul
|
||||
Medipol University Mega Hospital and University of Zurich / ETH Zurich.
|
||||
|
||||
In addition to the public datasets listed above, MedGemma was also trained on
|
||||
de-identified, licensed datasets or datasets collected internally at Google from
|
||||
consented participants.
|
||||
|
||||
* **CT dataset 1:** De-identified dataset of different axial CT studies across
|
||||
body parts (head, chest, abdomen) from a US-based radiology outpatient
|
||||
diagnostic center network.
|
||||
* **MRI dataset 1:** De-identified dataset of different axial multi-parametric
|
||||
MRI studies across body parts (head, abdomen, knee) from a US-based
|
||||
radiology outpatient diagnostic center network
|
||||
* **Ophthalmology dataset 1 (EyePACS):** De-identified dataset of fundus
|
||||
images from diabetic retinopathy screening.
|
||||
* **Dermatology dataset 1:** De-identified dataset of teledermatology skin
|
||||
condition images (both clinical and dermatoscopic) from Colombia.
|
||||
* **Dermatology dataset 2:** De-identified dataset of skin cancer images (both
|
||||
clinical and dermatoscopic) from Australia.
|
||||
* **Dermatology dataset 3:** De-identified dataset of non-diseased skin images
|
||||
from an internal data collection effort.
|
||||
* **Dermatology dataset 4**: De-identified dataset featuring multiple images
|
||||
and longitudinal visits and records from Japan.
|
||||
* **Dermatology dataset 5**: Dermatology dataset featuring unlabeled images.
|
||||
* **Dermatology dataset 6**: De-identified cases from adult patients with data
|
||||
representing Fitzpatrick 5 or 6 skin types
|
||||
* **Pathology dataset 1:** De-identified dataset of histopathology H\&E whole
|
||||
slide images created in collaboration with an academic research hospital and
|
||||
biobank in Europe. Comprises de-identified colon, prostate, and lymph nodes.
|
||||
* **Pathology dataset 2:** De-identified dataset of lung histopathology H\&E
|
||||
and IHC whole slide images created by a commercial biobank in the United
|
||||
States.
|
||||
* **Pathology dataset 3:** De-identified dataset of prostate and lymph node
|
||||
H\&E and IHC histopathology whole slide images created by a contract
|
||||
research organization in the United States.
|
||||
* **Pathology dataset 4:** De-identified dataset of histopathology whole slide
|
||||
images created in collaboration with a large, tertiary teaching hospital in
|
||||
the United States. Comprises a diverse set of tissue and stain types,
|
||||
predominantly H\&E.
|
||||
* **EHR dataset 1:** Question/answer dataset drawn from synthetic FHIR records
|
||||
created by [Synthea.](https://synthetichealth.github.io/synthea/) The test
|
||||
set includes 19 unique patients with 200 questions per patient divided into
|
||||
10 different categories.
|
||||
* **EHR dataset 2**: De-identified Lab Reports across different departments in
|
||||
Pathology such as Biochemistry, Clinical Pathology, Hematology, Microbiology
|
||||
and Serology
|
||||
* **EHR dataset 3**: De-identified Lab Reports across different departments in
|
||||
Pathology such as Biochemistry, Clinical Pathology, Hematology, Microbiology
|
||||
and Serology from at least 25 different labs
|
||||
* **EHR dataset 4**: Synthetic dataset of laboratory reports
|
||||
* **EHR dataset 5**: Synthetic dataset of approximately 60,000 health-relevant
|
||||
user queries
|
||||
|
||||
### Data citation
|
||||
|
||||
* **MIMIC-CXR:** Johnson, A., Pollard, T., Mark, R., Berkowitz, S., & Horng,
|
||||
S. (2024). MIMIC-CXR Database (version 2.1.0). PhysioNet.
|
||||
[https://physionet.org/content/mimic-cxr/2.1.0/](https://physionet.org/content/mimic-cxr/2.1.0/)
|
||||
*and* Johnson, Alistair E. W., Tom J. Pollard, Seth J. Berkowitz, Nathaniel
|
||||
R. Greenbaum, Matthew P. Lungren, Chih-Ying Deng, Roger G. Mark, and Steven
|
||||
Horng. 2019\. "MIMIC-CXR, a de-Identified Publicly Available Database of
|
||||
Chest Radiographs with Free-Text Reports." *Scientific Data 6* (1): 1–8.
|
||||
* **MS-CXR-T:** Bannur, S., Hyland, S., Liu, Q., Pérez-García, F., Ilse, M.,
|
||||
Coelho de Castro, D., Boecking, B., Sharma, H., Bouzid, K., Schwaighofer,
|
||||
A., Wetscherek, M. T., Richardson, H., Naumann, T., Alvarez Valle, J., &
|
||||
Oktay, O. (2023). MS-CXR-T: Learning to Exploit Temporal Structure for
|
||||
Biomedical Vision-Language Processing (version 1.0.0). PhysioNet.
|
||||
[https://doi.org/10.13026/pg10-j984](https://doi.org/10.13026/pg10-j984).
|
||||
* **ChestX-ray14:** Wang, Xiaosong, Yifan Peng, Le Lu, Zhiyong Lu,
|
||||
Mohammadhadi Bagheri, and Ronald M. Summers. "Chestx-ray8: Hospital-scale
|
||||
chest x-ray database and benchmarks on weakly-supervised classification and
|
||||
localization of common thorax diseases." In *Proceedings of the IEEE
|
||||
conference on computer vision and pattern recognition*, pp. 2097-2106.
|
||||
2017\.
|
||||
* **SLAKE:** Liu, Bo, Li-Ming Zhan, Li Xu, Lin Ma, Yan Yang, and Xiao-Ming Wu.
|
||||
2021.SLAKE: A Semantically-Labeled Knowledge-Enhanced Dataset for Medical
|
||||
Visual Question Answering."
|
||||
[http://arxiv.org/abs/2102.09542](http://arxiv.org/abs/2102.09542).
|
||||
* **PAD-UFES-20:** Pacheco, Andre GC, et al. "PAD-UFES-20: A skin lesion
|
||||
dataset composed of patient data and clinical images collected from
|
||||
smartphones." *Data in brief* 32 (2020): 106221\.
|
||||
* **SCIN:** Ward, Abbi, Jimmy Li, Julie Wang, Sriram Lakshminarasimhan, Ashley
|
||||
Carrick, Bilson Campana, Jay Hartford, et al. 2024\. "Creating an Empirical
|
||||
Dermatology Dataset Through Crowdsourcing With Web Search Advertisements."
|
||||
*JAMA Network Open 7* (11): e2446615–e2446615.
|
||||
* **TCGA:** The results shown here are in whole or part based upon data
|
||||
generated by the TCGA Research Network:
|
||||
[https://www.cancer.gov/tcga](https://www.cancer.gov/tcga).
|
||||
* **CAMELYON16:** Ehteshami Bejnordi, Babak, Mitko Veta, Paul Johannes van
|
||||
Diest, Bram van Ginneken, Nico Karssemeijer, Geert Litjens, Jeroen A. W. M.
|
||||
van der Laak, et al. 2017\. "Diagnostic Assessment of Deep Learning
|
||||
Algorithms for Detection of Lymph Node Metastases in Women With Breast
|
||||
Cancer." *JAMA 318* (22): 2199–2210.
|
||||
* **CAMELYON17:** Bandi, Peter, et al. "From detection of individual
|
||||
metastases to classification of lymph node status at the patient level: the
|
||||
camelyon17 challenge." *IEEE transactions on medical imaging* 38.2 (2018):
|
||||
550-560.
|
||||
* **Mendeley Digital Knee X-Ray:** Gornale, Shivanand; Patravali, Pooja
|
||||
(2020), "Digital Knee X-ray Images", Mendeley Data, V1, doi:
|
||||
10.17632/t9ndx37v5h.1
|
||||
* **VQA-RAD:** Lau, Jason J., Soumya Gayen, Asma Ben Abacha, and Dina
|
||||
Demner-Fushman. 2018\. "A Dataset of Clinically Generated Visual Questions
|
||||
and Answers about Radiology Images." *Scientific Data 5* (1): 1–10.
|
||||
* **Chest ImaGenome:** Wu, J., Agu, N., Lourentzou, I., Sharma, A., Paguio,
|
||||
J., Yao, J. S., Dee, E. C., Mitchell, W., Kashyap, S., Giovannini, A., Celi,
|
||||
L. A., Syeda-Mahmood, T., & Moradi, M. (2021). Chest ImaGenome Dataset
|
||||
(version 1.0.0). PhysioNet. RRID:SCR\_007345.
|
||||
[https://doi.org/10.13026/wv01-y230](https://doi.org/10.13026/wv01-y230)
|
||||
* **MedQA:** Jin, Di, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang,
|
||||
and Peter Szolovits. 2020\. "What Disease Does This Patient Have? A
|
||||
Large-Scale Open Domain Question Answering Dataset from Medical Exams."
|
||||
[http://arxiv.org/abs/2009.13081](http://arxiv.org/abs/2009.13081).
|
||||
* **MedMCQA:** Pal, Ankit, Logesh Kumar Umapathi, and Malaikannan
|
||||
Sankarasubbu. "Medmcqa: A large-scale multi-subject multi-choice dataset for
|
||||
medical domain question answering." *Conference on health, inference, and
|
||||
learning. PMLR,* 2022\.
|
||||
* **PubMedQA:** Jin, Qiao, et al. "Pubmedqa: A dataset for biomedical research
|
||||
question answering." *Proceedings of the 2019 conference on empirical
|
||||
methods in natural language processing and the 9th international joint
|
||||
conference on natural language processing (EMNLP-IJCNLP).* 2019\.
|
||||
* **LiveQA:** Abacha, Asma Ben, et al. "Overview of the medical question
|
||||
answering task at TREC 2017 LiveQA." *TREC.* 2017\.
|
||||
* **AfriMed-QA:** Olatunji, Tobi, Charles Nimo, Abraham Owodunni, Tassallah
|
||||
Abdullahi, Emmanuel Ayodele, Mardhiyah Sanni, Chinemelu Aka, et al. 2024\.
|
||||
"AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering
|
||||
Benchmark Dataset."
|
||||
[http://arxiv.org/abs/2411.15640](http://arxiv.org/abs/2411.15640).
|
||||
* **MedExpQA:** Alonso, I., Oronoz, M., & Agerri, R. (2024). MedExpQA:
|
||||
Multilingual Benchmarking of Large Language Models for Medical Question
|
||||
Answering. *arXiv preprint arXiv:2404.05590*. Retrieved from
|
||||
[https://arxiv.org/abs/2404.05590](https://arxiv.org/abs/2404.05590)
|
||||
* **MedXpertQA:** Zuo, Yuxin, Shang Qu, Yifei Li, Zhangren Chen, Xuekai Zhu,
|
||||
Ermo Hua, Kaiyan Zhang, Ning Ding, and Bowen Zhou. 2025\. "MedXpertQA:
|
||||
Benchmarking Expert-Level Medical Reasoning and Understanding."
|
||||
[http://arxiv.org/abs/2501.18362](http://arxiv.org/abs/2501.18362).
|
||||
* **HealthSearchQA:** Singhal, Karan, Shekoofeh Azizi, Tao Tu, S. Sara
|
||||
Mahdavi, Jason Wei, Hyung Won Chung, Nathan Scales et al. "Large language
|
||||
models encode clinical knowledge." *Nature* 620, no. 7972 (2023): 172-180.
|
||||
* **ISIC**: Gutman, David; Codella, Noel C. F.; Celebi, Emre; Helba, Brian;
|
||||
Marchetti, Michael; Mishra, Nabin; Halpern, Allan. "Skin Lesion Analysis
|
||||
toward Melanoma Detection: A Challenge at the International Symposium on
|
||||
Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging
|
||||
Collaboration (ISIC)". eprint [arXiv:1605.01397.
|
||||
2016](https://arxiv.org/abs/1605.01397)
|
||||
* **Mendeley Clinical Laboratory Test Reports:** Abdelmaksoud, Esraa;
|
||||
Gadallah, Ahmed; Asad, Ahmed (2022), “Clinical Laboratory Test Reports”,
|
||||
Mendeley Data, V2, doi: 10.17632/bygfmk4rx9.2
|
||||
* **CheXpert**: Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S.,
|
||||
Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., Seekins, J.,
|
||||
Mong, D. A., Halabi, S. S., Sandberg, J. K., Jones, R., Larson, D. B.,
|
||||
Langlotz, C. P., Patel, B. N., Lungren, M. P., & Ng, A. Y. (2019). CheXpert:
|
||||
A Large Chest Radiograph Dataset with Uncertainty Labels and Expert
|
||||
Comparison. arXiv:1901.07031
|
||||
* **CT-RATE:** Hamamci, I. E., Er, S., Almas, F., Simsek, A. G., Esirgun, S.
|
||||
N., Dogan, I., Dasdelen, M. F., Wittmann, B., Menze, B., et al. (2024).
|
||||
CT-RATE Dataset. Hugging Face.
|
||||
[https://huggingface.co/datasets/ibrahimhamamci/CT-RATE](https://huggingface.co/datasets/ibrahimhamamci/CT-RATE)
|
||||
and Hamamci, Ibrahim Ethem, Sezgin Er, Furkan Almas, Ayse Gulnihan Simsek,
|
||||
Sevval Nil Esirgun, Irem Dogan, Muhammed Furkan Dasdelen, Bastian Wittmann,
|
||||
et al. 2024\. "Developing Generalist Foundation Models from a Multimodal
|
||||
Dataset for 3D Computed Tomography." *arXiv preprint arXiv:2403.17834*.
|
||||
[https://arxiv.org/abs/2403.17834](https://arxiv.org/abs/2403.17834)
|
||||
* **EHRNoteQA**: Sunjun Kweon, Jiyoun Kim, Heeyoung Kwak, Dongchul Cha,
|
||||
Hangyul Yoon, Kwanghyun Kim, Jeewon Yang, Seunghyun Won, Edward Choi. (2024)
|
||||
“EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using
|
||||
Discharge Summaries.” arXiv:2402.16040
|
||||
|
||||
### De-identification/anonymization:
|
||||
|
||||
Google and its partners utilize datasets that have been rigorously anonymized or
|
||||
de-identified to ensure the protection of individual research participants and
|
||||
patient privacy.
|
||||
|
||||
## Implementation information
|
||||
|
||||
Details about the model internals.
|
||||
|
||||
### Software
|
||||
|
||||
Training was done using [JAX](https://github.com/jax-ml/jax).
|
||||
|
||||
JAX allows researchers to take advantage of the latest generation of hardware,
|
||||
including TPUs, for faster and more efficient training of large models.
|
||||
|
||||
## Use and limitations
|
||||
|
||||
### Intended use
|
||||
|
||||
MedGemma is an open multimodal generative AI model intended to be used as a
|
||||
starting point that enables more efficient development of downstream healthcare
|
||||
applications involving medical text and images. MedGemma is intended for
|
||||
developers in the life sciences and healthcare space. Developers are responsible
|
||||
for training, adapting, and making meaningful changes to MedGemma to accomplish
|
||||
their specific intended use. MedGemma models can be fine-tuned by developers
|
||||
using their own proprietary data for their specific tasks or solutions.
|
||||
|
||||
MedGemma is based on Gemma 3 and has been further trained on medical images and
|
||||
text. MedGemma enables further development in medical contexts (image and
|
||||
textual); however, the model has been trained using chest x-ray, histopathology,
|
||||
dermatology, fundus images, CT, MR, medical text/documents and electronic health
|
||||
records (EHR) data. Examples of tasks within MedGemma’s training include visual
|
||||
question answering pertaining to medical images, such as radiographs, document
|
||||
understanding, or providing answers to textual medical questions.
|
||||
|
||||
### Benefits
|
||||
|
||||
* Provides strong baseline medical image and text comprehension for models of
|
||||
its size.
|
||||
* This strong performance makes it efficient to adapt for downstream
|
||||
healthcare-based use cases, compared to models of similar size without
|
||||
medical data pre-training.
|
||||
* This adaptation may involve prompt engineering, grounding, agentic
|
||||
orchestration or fine-tuning depending on the use case, baseline validation
|
||||
requirements, and desired performance characteristics.
|
||||
|
||||
### Limitations
|
||||
|
||||
MedGemma is not intended to be used without appropriate validation, adaptation,
|
||||
and/or making meaningful modification by developers for their specific use case.
|
||||
The outputs generated by MedGemma are not intended to directly inform clinical
|
||||
diagnosis, patient management decisions, treatment recommendations, or any other
|
||||
direct clinical practice applications. All outputs from MedGemma should be
|
||||
considered preliminary and require independent verification, clinical
|
||||
correlation, and further investigation through established research and
|
||||
development methodologies.
|
||||
|
||||
MedGemma's multimodal capabilities have been primarily evaluated on single-image
|
||||
tasks. MedGemma has not been evaluated in use cases that involve comprehension
|
||||
of multiple images.
|
||||
|
||||
MedGemma has not been evaluated or optimized for multi-turn applications.
|
||||
|
||||
MedGemma's training may make it more sensitive to the specific prompt used than
|
||||
Gemma 3.
|
||||
|
||||
When adapting MedGemma developer should consider the following:
|
||||
|
||||
* **Bias in validation data:** As with any research, developers should ensure
|
||||
that any downstream application is validated to understand performance using
|
||||
data that is appropriately representative of the intended use setting for
|
||||
the specific application (e.g., age, sex, gender, condition, imaging device,
|
||||
etc).
|
||||
* **Data contamination concerns**: When evaluating the generalization
|
||||
capabilities of a large model like MedGemma in a medical context, there is a
|
||||
risk of data contamination, where the model might have inadvertently seen
|
||||
related medical information during its pre-training, potentially
|
||||
overestimating its true ability to generalize to novel medical concepts.
|
||||
Developers should validate MedGemma on datasets not publicly available or
|
||||
otherwise made available to non-institutional researchers to mitigate this
|
||||
risk.
|
||||
|
||||
### Release notes
|
||||
|
||||
#### MedGemma 4B IT
|
||||
|
||||
* Jan 13, 2026: Release of MedGemma 1.5 with improved medical reasoning,
|
||||
medical records interpretation and medical image interpretation
|
||||
* Jan 23, 2026: Updated generation config to use greedy decoding by default.
|
||||
Sampling can still be allowed by users to achieve previous functionality.
|
||||
Please see https://huggingface.co/docs/transformers/en/generation_strategies
|
||||
for details.
|
||||
|
||||
3
added_tokens.json
Normal file
3
added_tokens.json
Normal file
@@ -0,0 +1,3 @@
|
||||
{
|
||||
"<image_soft_token>": 262144
|
||||
}
|
||||
47
chat_template.jinja
Normal file
47
chat_template.jinja
Normal file
@@ -0,0 +1,47 @@
|
||||
{{ bos_token }}
|
||||
{%- if messages[0]['role'] == 'system' -%}
|
||||
{%- if messages[0]['content'] is string -%}
|
||||
{%- set first_user_prefix = messages[0]['content'] + '
|
||||
|
||||
' -%}
|
||||
{%- else -%}
|
||||
{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
|
||||
|
||||
' -%}
|
||||
{%- endif -%}
|
||||
{%- set loop_messages = messages[1:] -%}
|
||||
{%- else -%}
|
||||
{%- set first_user_prefix = "" -%}
|
||||
{%- set loop_messages = messages -%}
|
||||
{%- endif -%}
|
||||
{%- for message in loop_messages -%}
|
||||
{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
|
||||
{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
|
||||
{%- endif -%}
|
||||
{%- if (message['role'] == 'assistant') -%}
|
||||
{%- set role = "model" -%}
|
||||
{%- else -%}
|
||||
{%- set role = message['role'] -%}
|
||||
{%- endif -%}
|
||||
{{ '<start_of_turn>' + role + '
|
||||
' + (first_user_prefix if loop.first else "") }}
|
||||
{%- if message['content'] is string -%}
|
||||
{{ message['content'] | trim }}
|
||||
{%- elif message['content'] is iterable -%}
|
||||
{%- for item in message['content'] -%}
|
||||
{%- if item['type'] == 'image' -%}
|
||||
{{ '<start_of_image>' }}
|
||||
{%- elif item['type'] == 'text' -%}
|
||||
{{ item['text'] | trim }}
|
||||
{%- endif -%}
|
||||
{%- endfor -%}
|
||||
{%- else -%}
|
||||
{{ raise_exception("Invalid content type") }}
|
||||
{%- endif -%}
|
||||
{{ '<end_of_turn>
|
||||
' }}
|
||||
{%- endfor -%}
|
||||
{%- if add_generation_prompt -%}
|
||||
{{'<start_of_turn>model
|
||||
'}}
|
||||
{%- endif -%}
|
||||
99
config.json
Normal file
99
config.json
Normal file
@@ -0,0 +1,99 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Gemma3ForConditionalGeneration"
|
||||
],
|
||||
"boi_token_index": 255999,
|
||||
"dtype": "bfloat16",
|
||||
"torch_dtype": "bfloat16",
|
||||
"eoi_token_index": 256000,
|
||||
"eos_token_id": [
|
||||
1,
|
||||
106
|
||||
],
|
||||
"image_token_index": 262144,
|
||||
"initializer_range": 0.02,
|
||||
"mm_tokens_per_image": 256,
|
||||
"model_type": "gemma3",
|
||||
"text_config": {
|
||||
"_sliding_window_pattern": 6,
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"attn_logit_softcapping": null,
|
||||
"dtype": "bfloat16",
|
||||
"final_logit_softcapping": null,
|
||||
"head_dim": 256,
|
||||
"hidden_activation": "gelu_pytorch_tanh",
|
||||
"hidden_size": 2560,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 10240,
|
||||
"layer_types": [
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"full_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"full_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"full_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"full_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"full_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention",
|
||||
"sliding_attention"
|
||||
],
|
||||
"max_position_embeddings": 131072,
|
||||
"model_type": "gemma3_text",
|
||||
"num_attention_heads": 8,
|
||||
"num_hidden_layers": 34,
|
||||
"num_key_value_heads": 4,
|
||||
"query_pre_attn_scalar": 256,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_local_base_freq": 10000,
|
||||
"rope_scaling": {
|
||||
"factor": 8.0,
|
||||
"rope_type": "linear"
|
||||
},
|
||||
"rope_theta": 1000000,
|
||||
"sliding_window": 1024,
|
||||
"use_bidirectional_attention": false,
|
||||
"use_cache": true,
|
||||
"vocab_size": 262208
|
||||
},
|
||||
"transformers_version": "4.57.1",
|
||||
"vision_config": {
|
||||
"attention_dropout": 0.0,
|
||||
"dtype": "bfloat16",
|
||||
"hidden_act": "gelu_pytorch_tanh",
|
||||
"hidden_size": 1152,
|
||||
"image_size": 896,
|
||||
"intermediate_size": 4304,
|
||||
"layer_norm_eps": 1e-06,
|
||||
"model_type": "siglip_vision_model",
|
||||
"num_attention_heads": 16,
|
||||
"num_channels": 3,
|
||||
"num_hidden_layers": 27,
|
||||
"patch_size": 14,
|
||||
"vision_use_head": false
|
||||
}
|
||||
}
|
||||
8
generation_config.json
Normal file
8
generation_config.json
Normal file
@@ -0,0 +1,8 @@
|
||||
{
|
||||
"cache_implementation": "hybrid",
|
||||
"eos_token_id": [
|
||||
1,
|
||||
106
|
||||
],
|
||||
"transformers_version": "4.57.1"
|
||||
}
|
||||
3
google_medgemma-1.5-4b-it-F16.gguf
Normal file
3
google_medgemma-1.5-4b-it-F16.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:b67d224a4f55b6b879c84113a20ba518c004f5fe46b7bf872f0ae04d78579d7c
|
||||
size 7767803904
|
||||
3
google_medgemma-1.5-4b-it-IQ3_M.gguf
Normal file
3
google_medgemma-1.5-4b-it-IQ3_M.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:332d0805874bbbd87866973ba33c392c2c3b93151245a254cfa891aa2d489188
|
||||
size 1986802944
|
||||
3
google_medgemma-1.5-4b-it-IQ3_S.gguf
Normal file
3
google_medgemma-1.5-4b-it-IQ3_S.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:30dce573c9be659af738f2e6a38a436f736be4e650c77a0d1d2f84aa6b904c1d
|
||||
size 1937364224
|
||||
3
google_medgemma-1.5-4b-it-IQ4_NL.gguf
Normal file
3
google_medgemma-1.5-4b-it-IQ4_NL.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:17f279d3d249b9dc507ec9384be2e4cee8eb71022701f00dd8c00e922f24c9b8
|
||||
size 2376619264
|
||||
3
google_medgemma-1.5-4b-it-IQ4_XS.gguf
Normal file
3
google_medgemma-1.5-4b-it-IQ4_XS.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:cf717f1394db26d8abbc8fabe0effbb44256f53a11112a29b9a520f4af44d082
|
||||
size 2279625984
|
||||
3
google_medgemma-1.5-4b-it-Q2_K.gguf
Normal file
3
google_medgemma-1.5-4b-it-Q2_K.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:ae07d73e5a7fc651506c17cb1d490634675c8659d4bae6d6d452713e4acc4e77
|
||||
size 1729164544
|
||||
3
google_medgemma-1.5-4b-it-Q3_K.gguf
Normal file
3
google_medgemma-1.5-4b-it-Q3_K.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c0f7ed2fb013bb99b1219c2906b090e9cc18491fb27359f1e2c75fbe120ccf88
|
||||
size 2098459904
|
||||
3
google_medgemma-1.5-4b-it-Q3_K_L.gguf
Normal file
3
google_medgemma-1.5-4b-it-Q3_K_L.gguf
Normal file
@@ -0,0 +1,3 @@
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29
preprocessor_config.json
Normal file
29
preprocessor_config.json
Normal file
@@ -0,0 +1,29 @@
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{
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}
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4
processor_config.json
Normal file
4
processor_config.json
Normal file
@@ -0,0 +1,4 @@
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{
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33
special_tokens_map.json
Normal file
33
special_tokens_map.json
Normal file
@@ -0,0 +1,33 @@
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{
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
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version https://git-lfs.github.com/spec/v1
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oid sha256:7d4046bf0505a327dd5a0abbb427ecd4fc82f99c2ceaa170bc61ecde12809b0c
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3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
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size 4689074
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51346
tokenizer_config.json
Normal file
51346
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user