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features
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Description

The Gemma family consists of advanced, lightweight models developed using the same innovative research and technology as the Gemini models. These cutting-edge models are equipped with robust security features that promote responsible and trustworthy AI applications, achieved through carefully curated data sets and thorough refinements. Notably, Gemma models excel in their various sizes—2B, 7B, 9B, and 27B—often exceeding the performance of some larger open models. With the introduction of Keras 3.0, users can experience effortless integration with JAX, TensorFlow, and PyTorch, providing flexibility in framework selection based on specific tasks. Designed for peak performance and remarkable efficiency, Gemma 2 is specifically optimized for rapid inference across a range of hardware platforms. Furthermore, the Gemma family includes diverse models that cater to distinct use cases, ensuring they adapt effectively to user requirements. These lightweight language models feature a decoder and have been trained on an extensive array of textual data, programming code, and mathematical concepts, which enhances their versatility and utility in various applications.

Description

MedGemma is an innovative suite of Gemma 3 variants specifically designed to excel in the analysis of medical texts and images. This resource empowers developers to expedite the creation of AI applications focused on healthcare. Currently, MedGemma offers two distinct variants: a multimodal version with 4 billion parameters and a text-only version featuring 27 billion parameters. The 4B version employs a SigLIP image encoder, which has been meticulously pre-trained on a wealth of anonymized medical data, such as chest X-rays, dermatological images, ophthalmological images, and histopathological slides. Complementing this, its language model component is trained on a wide array of medical datasets, including radiological images and various pathology visuals. MedGemma 4B can be accessed in both pre-trained versions, denoted by the suffix -pt, and instruction-tuned versions, marked by the suffix -it. For most applications, the instruction-tuned variant serves as the optimal foundation to build upon, making it particularly valuable for developers. Overall, MedGemma represents a significant advancement in the integration of AI within the medical field.

API Access

Has API Yes 

API Access

Has API No 

Screenshots View All

Screenshots View All

Integrations

Gemini Enterprise Agent Platform Yes 
Hugging Face Yes 
C++ Yes 
CSS Yes 
Dr7.ai No 
Gemma 3 No 
Google Cloud Platform Yes 
Java Yes 
JavaScript Yes 
Kaggle Yes 
LM-Kit.NET Yes 
LangChain Yes 
Molmo Yes 
Nebius Token Factory Yes 
Pipeshift Yes 
PyTorch Yes 
R Yes 
SQL Yes 
VESSL AI Yes 
nexos.ai Yes 

Integrations

Gemini Enterprise Agent Platform Yes 
Hugging Face Yes 
C++ No 
CSS No 
Dr7.ai Yes 
Gemma 3 Yes 
Google Cloud Platform No 
Java No 
JavaScript No 
Kaggle No 
LM-Kit.NET No 
LangChain No 
Molmo No 
Nebius Token Factory No 
Pipeshift No 
PyTorch No 
R No 
SQL No 
VESSL AI No 
nexos.ai No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based No 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person Yes 

Vendor Details

Company Name

Google

Country

United States

Website

ai.google.dev/gemma

Vendor Details

Company Name

Google DeepMind

Founded

2010

Country

United Kingdom

Website

deepmind.google/models/gemma/medgemma/

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