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inclusionAI/LLaDA2.X

LLaDA2.0 is the diffusion language model series developed by InclusionAI team, Ant Group.

#Language model
Visit GitHub
528
29
2 contributors
Apache License 2.0
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How fast inclusionAI/LLaDA2.X is gaining stars, and where it lands on Trendshift and GitHub Trending

Activity and trending

Stars, forks and merged pull requests each day or month, lined up with the repository's rank on Trendshift Trending

Aug 20 – Oct 19, 2026

Stars +18 in 61 days

012
2

Forks +1 in 61 days

01
1

Merged PRs 0 in 61 days

01

Trendshift Trendingwhere the repository ranked each day it made the top 25

Not in the Trendshift top 25 in this period

Aug 20Aug 28Sep 5Sep 13Sep 21Sep 29Oct 7Oct 15
The left and right arrow keys step from day to day; Home and End go to the first and the last

Stars, forks, merged pull requests and Trendshift Trending rank, by day
DayStars gainedForks gainedMerged PRs
Thu, Aug 20, 2026+1+00
Fri, Aug 21, 2026+0+00
Sat, Aug 22, 2026+0+00
Sun, Aug 23, 2026+2+10
Mon, Aug 24, 2026+0+00
Tue, Aug 25, 2026+1+00
Wed, Aug 26, 2026+0+00
Thu, Aug 27, 2026+1+00
Fri, Aug 28, 2026+1+00
Sat, Aug 29, 2026+0+00
Sun, Aug 30, 2026+0+00
Mon, Aug 31, 2026+0+00
Tue, Sep 1, 2026+0+00
Wed, Sep 2, 2026+0+00
Thu, Sep 3, 2026+0+00
Fri, Sep 4, 2026+2+00
Sat, Sep 5, 2026+0+00
Sun, Sep 6, 2026+1+00
Mon, Sep 7, 2026+1+00
Tue, Sep 8, 2026+1+00
Wed, Sep 9, 2026+2+00
Thu, Sep 10, 2026+1+00
Fri, Sep 11, 2026+0+00
Sat, Sep 12, 2026+1+00
Sun, Sep 13, 2026+1+00
Mon, Sep 14, 2026+0+00
Tue, Sep 15, 2026+0+00
Wed, Sep 16, 2026+0+00
Thu, Sep 17, 2026+0+00
Fri, Sep 18, 2026+0+00
Sat, Sep 19, 2026+1+00
Sun, Sep 20, 2026+0+00
Mon, Sep 21, 2026+0+00
Tue, Sep 22, 2026+0+00
Wed, Sep 23, 2026+0+00
Thu, Sep 24, 2026+0+00
Fri, Sep 25, 2026+0+00
Sat, Sep 26, 2026+0+00
Sun, Sep 27, 2026+0+00
Mon, Sep 28, 2026+0+00
Tue, Sep 29, 2026+0+00
Wed, Sep 30, 2026+0+00
Thu, Oct 1, 2026+0+00
Fri, Oct 2, 2026+0+00
Sat, Oct 3, 2026+0+00
Sun, Oct 4, 2026+0+00
Mon, Oct 5, 2026+0+00
Tue, Oct 6, 2026+0+00
Wed, Oct 7, 2026+0+00
Thu, Oct 8, 2026+0+00
Fri, Oct 9, 2026+0+00
Sat, Oct 10, 2026+0+00
Sun, Oct 11, 2026+0+00
Mon, Oct 12, 2026+0+00
Tue, Oct 13, 2026+0+00
Wed, Oct 14, 2026+0+00
Thu, Oct 15, 2026+0+00
Fri, Oct 16, 2026+0+00
Sat, Oct 17, 2026+0+00
Sun, Oct 18, 2026+0+00
Mon, Oct 19, 2026+1+00
Total+18+10
  • 1#1 on the list
  • 7Rank that day
  • Not trending

Social mentions

Recent discussions about this repository across the web
  1. Jul 255 posts6 posts

    • On agentic benchmarks, LLaDA2.2-flash comes out ahead of Ling-2.6-flash on τ²-Bench (592.80 vs 334.90). Furthermore, LLaDA2.2-flash (fast mode) achieves 705.30.

      Read full on X, posted by @omarsar0

      @omarsar0 · x.com

    • It's also built for production-scale agent workflows. → Native 128K context window → Block Routing for more efficient long-context inference → 1.64× average BF16 throughput over Ling-2.6-flash And it…

      Read full on X, posted by @Parul_Gautam7

      @Parul_Gautam7 · x.com

    • The numbers → 53.83 average across 7 agentic benchmarks → Leads on τ² Bench: 80.33 → Leads on PinchBench: 81.66 → Leads on MCP Atlas: 46.21 → 1.64x average throughput over Ling 2.6 flash across 11…

      Read full on X, posted by @ginacostag_

      @ginacostag_ · x.com

    • Ant Group just built a diffusion model that performs surgery on its own output 🤯 LLaDA2.2-flash edits mid-generation and still keeps block-parallel speed. It catches its own mistakes, deletes them,…

      Read full on X, posted by @dr_cintas

      @dr_cintas · x.com

    • Benchmarks: LLaDA2.2 (diffusion) vs. Ling-2.6-flash (autoregressive) - 53.83 vs. 55.74 avg across 7 agentic benchmarks (1.91 gap) - LLaDA wins τ²-Bench, PinchBench, MCP-Atlas - 1.64x faster - native…

      Read full on X, posted by @Alifkhanzxx

      @Alifkhanzxx · x.com

    • Every AI agent you use today runs on autoregressive models. One token at a time, left to right. That architecture was built for conversation, not for agents that plan, call tools, and self-correct…

      Read full on X, posted by @heyshrutimishra

      @heyshrutimishra · x.com

  2. Jul 241 post

    • Benchmarks: LLaDA2.2 (diffusion) vs. Ling-2.6-flash (autoregressive): • 53.83 vs. 55.74 average across 7 agentic benchmarks (just a 1.91-point gap) • Wins on τ²-Bench, PinchBench, and MCP-Atlas •…

      Read full on X, posted by @Prathkum

      @Prathkum · x.com

  3. Jul 231 post

    • inclusionAI/LLaDA2.2-flash · Hugging Face

      Read full on Reddit

      r/LocalLLaMA

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