TILDE

TILDE

ielab
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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.

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.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

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

Audience

Academic researchers and developers searching for a tool to implement efficient and scalable passage re-ranking and expansion techniques

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

$2 per 1M (input)
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

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Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Alibaba
Founded: 1999
China
qwen.ai/blog

Company Information

ielab
United States
github.com/ielab/TILDE/tree/main

Alternatives

Alternatives

ColBERT

ColBERT

Future Data Systems
GPT-5.6 Sol

GPT-5.6 Sol

OpenAI
Qwen3.5

Qwen3.5

Alibaba
RankLLM

RankLLM

Castorini

Categories

Categories

Integrations

Hugging Face
Python
Alibaba Cloud
Alibaba Cloud Model Studio
Cherry Studio
Cline
ClinePass
Happy Shrimp 1.0
Hermes Agent
Model Context Protocol (MCP)
ModelScope
Novita AI
Odysseus
OfoxAI
OpenClaw
Qwen
Qwen Code
Qwen Studio
QwenCloud
QwenWork

Integrations

Hugging Face
Python
Alibaba Cloud
Alibaba Cloud Model Studio
Cherry Studio
Cline
ClinePass
Happy Shrimp 1.0
Hermes Agent
Model Context Protocol (MCP)
ModelScope
Novita AI
Odysseus
OfoxAI
OpenClaw
Qwen
Qwen Code
Qwen Studio
QwenCloud
QwenWork
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Claim Qwen3.8-Flash-Next and update features and information
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