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ease
features
design
support

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Description

EmbeddingGemma is a versatile multilingual text embedding model with 308 million parameters, designed to be lightweight yet effective, allowing it to operate seamlessly on common devices like smartphones, laptops, and tablets. This model, based on the Gemma 3 architecture, is capable of supporting more than 100 languages and can handle up to 2,000 input tokens, utilizing Matryoshka Representation Learning (MRL) for customizable embedding sizes of 768, 512, 256, or 128 dimensions, which balances speed, storage, and accuracy. With its GPU and EdgeTPU-accelerated capabilities, it can generate embeddings in a matter of milliseconds—taking under 15 ms for 256 tokens on EdgeTPU—while its quantization-aware training ensures that memory usage remains below 200 MB without sacrificing quality. Such characteristics make it especially suitable for immediate, on-device applications, including semantic search, retrieval-augmented generation (RAG), classification, clustering, and similarity detection. Whether used for personal file searches, mobile chatbot functionality, or specialized applications, its design prioritizes user privacy and efficiency. Consequently, EmbeddingGemma stands out as an optimal solution for a variety of real-time text processing needs.

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

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Gemma 3
Gemma 4
Dr7.ai
Gemini Enterprise Agent Platform
Gemma 2
Hugging Face

Integrations

Gemma 3
Gemma 4
Dr7.ai
Gemini Enterprise Agent Platform
Gemma 2
Hugging Face

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

ai.google.dev/gemma/docs/embeddinggemma

Vendor Details

Company Name

Google DeepMind

Founded

2010

Country

United Kingdom

Website

deepmind.google/models/gemma/medgemma/

Product Features

Product Features

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