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Description
CodeGemma represents an impressive suite of efficient and versatile models capable of tackling numerous coding challenges, including middle code completion, code generation, natural language processing, mathematical reasoning, and following instructions. It features three distinct model types: a 7B pre-trained version designed for code completion and generation based on existing code snippets, a 7B variant fine-tuned for translating natural language queries into code and adhering to instructions, and an advanced 2B pre-trained model that offers code completion speeds up to twice as fast. Whether you're completing lines, developing functions, or crafting entire segments of code, CodeGemma supports your efforts, whether you're working in a local environment or leveraging Google Cloud capabilities. With training on an extensive dataset comprising 500 billion tokens predominantly in English, sourced from web content, mathematics, and programming languages, CodeGemma not only enhances the syntactical accuracy of generated code but also ensures its semantic relevance, thereby minimizing mistakes and streamlining the debugging process. This powerful tool continues to evolve, making coding more accessible and efficient for developers everywhere.
Description
DiffusionGemma is an innovative open model that investigates text diffusion, representing a remarkably rapid method for generating text. Released under the Apache 2.0 license, this 26 billion parameter Mixture of Experts (MoE) model advances beyond the usual sequential token generation typical of autoregressive models. Instead, it produces entire blocks of text at once, achieving text generation speeds that are up to four times faster on GPUs. Drawing from the parameter efficiency of the Gemma 4 family and Gemini Diffusion research, DiffusionGemma incorporates a unique diffusion head that enhances generation speed significantly. It is particularly aimed at researchers and developers looking to optimize speed-sensitive, interactive local workflows, including in-line editing, swift iterations, and non-linear narrative forms. By reallocating the decode bottleneck from memory bandwidth to computational power, it can produce over 1,000 tokens per second on a single NVIDIA H100 and more than 700 tokens per second on an NVIDIA GeForce RTX 5090. This breakthrough allows for a new level of efficiency in text generation that could reshape various applications in natural language processing.
API Access
Has API
No
API Access
Has API
No
Integrations
C
Yes
C++
Yes
CSS
Yes
Clojure
Yes
Elixir
Yes
F#
Yes
GaiaNet
Yes
Gemini Enterprise Agent Platform
No
Gemma
No
Go
Yes
Integrations
C
No
C++
No
CSS
No
Clojure
No
Elixir
No
F#
No
GaiaNet
No
Gemini Enterprise Agent Platform
Yes
Gemma
Yes
Go
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
Free
Free Trial
No
Free Version
Yes
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
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
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
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
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Country
United States
Website
ai.google.dev/gemma/docs/codegemma
Vendor Details
Company Name
Founded
1998
Country
United States
Website
blog.google/innovation-and-ai/technology/developers-tools/diffusion-gemma-faster-text-generation/
