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jingyaogong/minimind
🧠Train a 64M-parameter LLM from scratch in just 2h!
Current momentum
Stars
64K
+183 since sync
Commits
0
in period
Active contributors
0
+-75%
Issues closed
2
343.3h avg. resolution
Recorded trajectory
Movement you can verify over time.
Loading daily momentum history.
Behind the number
A score you can inspect.
Each signal contributes points to the total. These are capped GitHub samples, not complete activity counts or proof of adoption.
Development activity28.9 / 40 pts
0 commits · 3 opened PRs · 2 closed issues in 7 days
24 + 22 × ln(1 + weekly weighted activity). Activity = commits + 2 × opened PRs + closed issues. Signal bounded to 0–100, then weighted 40%.
Contributor breadth3.2 / 25 pts
0 sampled contributors in 7 days · -75% contributor-growth input
24 + 25 × ln(1 + contributors × √(7 / days)) + 0.15 × growth. Growth uses the 30-day windows; capped samples use a neutral 0 input. Signal bounded to 0–100, then weighted 25%.
Issue resolution5.8 / 20 pts
343.3 hours resolution input
105 − 9 × log₂(1 + hours). Uses sampled closed issues, not first-response time. With no resolved issues, the model uses 168 hours. Signal bounded to 0–100, then weighted 20%.
Release cadence0.0 / 15 pts
90 days cadence input
105 − 5 × cadence days. With fewer than two published releases, the model uses time since the last push as a proxy. Signal bounded to 0–100, then weighted 15%.
What changed in the recorded sample?
Loading recorded observations…
8.3K forks
Downstream adoption
67 open issues
Community demand
90 day cadence
Shipping consistency