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Superone77/FlashLoop

FlashLoop: Fast and Memory-Efficient Looped Transformers via Lazy Updates

#LLM inference
Visit GitHub
Python
25
4
1 contributors
MIT License
website
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Momentum

How fast Superone77/FlashLoop 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

Sep 24 – Nov 5, 2026

Stars +24 in 43 days

024
4

Forks +4 in 43 days

01
1

Merged PRs 0 in 43 days

01

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

Not in the Trendshift top 25 in this period

Sep 24Sep 30Oct 6Oct 12Oct 18Oct 24Oct 30Nov 5
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, Sep 24, 2026+0+00
Fri, Sep 25, 2026+0+00
Sat, Sep 26, 2026+3+10
Sun, Sep 27, 2026+4+00
Mon, Sep 28, 2026+1+00
Tue, Sep 29, 2026+1+00
Wed, Sep 30, 2026+0+00
Thu, Oct 1, 2026+0+00
Fri, Oct 2, 2026+2+00
Sat, Oct 3, 2026+0+00
Sun, Oct 4, 2026+1+00
Mon, Oct 5, 2026+1+00
Tue, Oct 6, 2026+0+00
Wed, Oct 7, 2026+1+00
Thu, Oct 8, 2026+0+00
Fri, Oct 9, 2026+0+10
Sat, Oct 10, 2026+1+00
Sun, Oct 11, 2026+0+10
Mon, Oct 12, 2026+0+00
Tue, Oct 13, 2026+0+00
Wed, Oct 14, 2026+0+00
Thu, Oct 15, 2026+2+00
Fri, Oct 16, 2026+0+00
Sat, Oct 17, 2026+0+00
Sun, Oct 18, 2026+0+00
Mon, Oct 19, 2026+0+00
Tue, Oct 20, 2026+0+10
Wed, Oct 21, 2026+0+00
Thu, Oct 22, 2026+4+00
Fri, Oct 23, 2026+0+00
Sat, Oct 24, 2026+0+00
Sun, Oct 25, 2026+0+00
Mon, Oct 26, 2026+0+00
Tue, Oct 27, 2026+1+00
Wed, Oct 28, 2026+1+00
Thu, Oct 29, 2026+0+00
Fri, Oct 30, 2026+0+00
Sat, Oct 31, 2026+0+00
Sun, Nov 1, 2026+0+00
Mon, Nov 2, 2026+0+00
Tue, Nov 3, 2026+1+00
Wed, Nov 4, 2026+0+00
Thu, Nov 5, 2026+0+00
Total+24+40
  • 1#1 on the list
  • 7Rank that day
  • Not trending

Social mentions

Recent discussions about this repository across the web
  1. Sep 251 post

    • Looped Transformers enable deeper computation without more parameters. But what is the cost? 🙋 Every loop adds compute and KV cache. The key to inference efficiency is exploiting cross-loop…

      Read full on X, posted by @WanqiYang1

      @WanqiYang1 · x.com

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