Qwen3.8-Flash-NextAlibaba
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TILDEielab
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About
Qwen3.8-Flash-Next is an open-weight multimodal Mixture-of-Experts model and an early preview of the architecture planned for Qwen4. It systematically upgrades attention, residual connections, embeddings, and optimization to improve capability, computational efficiency, model capacity, and training stability. Its hybrid architecture combines Gated DeltaNet, which efficiently compresses historical information, with Qwen Sparse Attention, which selects important context at the micro-block level to reduce attention and indexing costs on long sequences. Gated Residual widens the residual stream into four branches and dynamically controls information flow across layers, while N-gram Embedding adds large-scale local-pattern memory with very little extra per-token computation and can be offloaded to host memory. The model uses a 125B-parameter main network plus 51B N-gram embedding parameters, while activating only 6B parameters per token.
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About
TILDE (Term Independent Likelihood moDEl) is a passage re-ranking and expansion framework built on BERT, designed to enhance retrieval performance by combining sparse term matching with deep contextual representations. The original TILDE model pre-computes term weights across the entire BERT vocabulary, which can lead to large index sizes. To address this, TILDEv2 introduces a more efficient approach by computing term weights only for terms present in expanded passages, resulting in indexes that are 99% smaller than those of the original TILDE. This efficiency is achieved by leveraging TILDE as a passage expansion model, where passages are expanded using top-k terms (e.g., top 200) to enrich their content. It provides scripts for indexing collections, re-ranking BM25 results, and training models using datasets like MS MARCO.
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
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Audience
Developers, researchers, and AI teams seeking to run or study an efficient multimodal open-weight model with long-context reasoning, coding, multilingual, and agentic capabilities
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Audience
Academic researchers and developers searching for a tool to implement efficient and scalable passage re-ranking and expansion techniques
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Support
Phone Support
24/7 Live Support
Online
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Support
Phone Support
24/7 Live Support
Online
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API
Offers API
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API
Offers API
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Screenshots and Videos |
Screenshots and Videos |
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Pricing
$2 per 1M (input)
Free Version
Free Trial
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Pricing
No information available.
Free Version
Free Trial
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Reviews/
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Reviews/
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Training
Documentation
Webinars
Live Online
In Person
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Training
Documentation
Webinars
Live Online
In Person
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Company InformationAlibaba
Founded: 1999
China
qwen.ai/blog
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Company Informationielab
United States
github.com/ielab/TILDE/tree/main
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Categories |
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Integrations
Hugging Face
Python
Alibaba Cloud
Alibaba Cloud Model Studio
Cherry Studio
Cline
ClinePass
Happy Shrimp 1.0
Hermes Agent
Model Context Protocol (MCP)
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Integrations
Hugging Face
Python
Alibaba Cloud
Alibaba Cloud Model Studio
Cherry Studio
Cline
ClinePass
Happy Shrimp 1.0
Hermes Agent
Model Context Protocol (MCP)
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