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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

Introducing Gemma 3n, our cutting-edge open multimodal model designed specifically for optimal on-device performance and efficiency. With a focus on responsive and low-footprint local inference, Gemma 3n paves the way for a new generation of intelligent applications that can be utilized on the move. It has the capability to analyze and respond to a blend of images and text, with plans to incorporate video and audio functionalities in the near future. Developers can create smart, interactive features that prioritize user privacy and function seamlessly without an internet connection. The model boasts a mobile-first architecture, significantly minimizing memory usage. Co-developed by Google's mobile hardware teams alongside industry experts, it maintains a 4B active memory footprint while also offering the flexibility to create submodels for optimizing quality and latency. Notably, Gemma 3n represents our inaugural open model built on this revolutionary shared architecture, enabling developers to start experimenting with this advanced technology today in its early preview. As technology evolves, we anticipate even more innovative applications to emerge from this robust framework.

Description

Qwen3.8-Flash-Next represents an open-weight multimodal Mixture-of-Experts architecture and serves as an initial glimpse into the design intended for Qwen4. This model strategically enhances attention mechanisms, residual pathways, embeddings, and optimization techniques to boost its capabilities, improve computational efficiency, expand model capacity, and ensure training stability. Its innovative hybrid architecture merges Gated DeltaNet, which adeptly compresses past information, with Qwen Sparse Attention, enabling the selection of significant context at a micro-block level to lessen both attention and indexing costs associated with lengthy sequences. The Gated Residual feature broadens the residual pathway into four streams, dynamically managing the flow of information across different layers. Additionally, the N-gram Embedding integrates large-scale local-pattern memory with minimal added computation per token, and it can be transferred to host memory for further efficiency. The model is structured around a 125B-parameter main network supplemented by 51B parameters dedicated to N-gram embeddings, activating only 6B parameters for each token processed. This sophisticated framework highlights the ongoing advancements in machine learning architectures, setting a promising stage for future developments.

API Access

Has API No 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Hugging Face Yes 
Ollama Yes 
Alibaba Cloud No 
Cherry Studio No 
Cline No 
ClinePass No 
Gemma Yes 
Google AI Edge Gallery Yes 
Hermes Agent No 
Keras Yes 
Model Context Protocol (MCP) No 
Novita AI No 
Odysseus No 
OfoxAI No 
OpenCode Yes 
PyTorch Yes 
Python No 
Qwen No 
Qwen Code No 
QwenCloud No 

Integrations

Hugging Face Yes 
Ollama Yes 
Alibaba Cloud Yes 
Cherry Studio Yes 
Cline Yes 
ClinePass Yes 
Gemma No 
Google AI Edge Gallery No 
Hermes Agent Yes 
Keras No 
Model Context Protocol (MCP) Yes 
Novita AI Yes 
Odysseus Yes 
OfoxAI Yes 
OpenCode No 
PyTorch No 
Python Yes 
Qwen Yes 
Qwen Code Yes 
QwenCloud Yes 

Pricing Details

No price information available.
Free Trial Yes 
Free Version No 

Pricing Details

$2 per 1M (input)
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 Yes 
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 Yes 
Live Training (Online) No 
In Person Yes 

Types of Training

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

Vendor Details

Company Name

Google DeepMind

Founded

2010

Country

United Kingdom

Website

deepmind.google/models/gemma/gemma-3n/

Vendor Details

Company Name

Alibaba

Founded

1999

Country

China

Website

qwen.ai/blog

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