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features
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support

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Description

Ai2 OLMoE is a completely open-source mixture-of-experts language model that operates entirely on-device, ensuring that you can experiment with the model in a private and secure manner. This application is designed to assist researchers in advancing on-device intelligence and to allow developers to efficiently prototype innovative AI solutions without the need for cloud connectivity. OLMoE serves as a highly efficient variant within the Ai2 OLMo model family. Discover the capabilities of state-of-the-art local models in performing real-world tasks, investigate methods to enhance smaller AI models, and conduct local tests of your own models utilizing our open-source codebase. Furthermore, you can seamlessly integrate OLMoE into various iOS applications, as the app prioritizes user privacy and security by functioning entirely on-device. Users can also easily share the outcomes of their interactions with friends or colleagues. Importantly, both the OLMoE model and the application code are fully open source, offering a transparent and collaborative approach to AI development. By leveraging this model, developers can contribute to the growing field of on-device AI while maintaining high standards of user privacy.

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

Alibaba Cloud No 
Alibaba Cloud Model Studio No 
Cherry Studio No 
Cline No 
ClinePass No 
Happy Shrimp 1.0 No 
Hermes Agent No 
Hugging Face No 
Model Context Protocol (MCP) No 
ModelScope No 
Molmo 2 Yes 
Novita AI No 
Odysseus No 
OfoxAI No 
Ollama No 
OpenClaw No 
Python No 
Qwen Code No 
Qwen Studio No 
QwenCloud No 

Integrations

Alibaba Cloud Yes 
Alibaba Cloud Model Studio Yes 
Cherry Studio Yes 
Cline Yes 
ClinePass Yes 
Happy Shrimp 1.0 Yes 
Hermes Agent Yes 
Hugging Face Yes 
Model Context Protocol (MCP) Yes 
ModelScope Yes 
Molmo 2 No 
Novita AI Yes 
Odysseus Yes 
OfoxAI Yes 
Ollama Yes 
OpenClaw Yes 
Python Yes 
Qwen Code Yes 
Qwen Studio Yes 
QwenCloud Yes 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

$2 per 1M (input)
Free Trial No 
Free Version No 

Deployment

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

The Allen Institute for Artificial Intelligence

Founded

2014

Country

United States

Website

allenai.org

Vendor Details

Company Name

Alibaba

Founded

1999

Country

China

Website

qwen.ai/blog

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