labml.ai
@labmlai • Tools to help deep learning researchers. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
99%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
12 repos
Visible snapshot
1 repositories updated in the last 90 days.
Leading language
Python
Portfolio mix
Python (4), TypeScript (4), Jupyter Notebook (1)
Average size
5.9K
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2026-07-08(49d ago)GitHub API fallback12 repositories
99%
of the visible star count comes from this organization's top three repositories.
5.9K
stars per repository in this same snapshot.
Python
is the most common language here, with 1 repositories updated in the last 90 days.
Why this rank
This organization stands out because one flagship repo drives 95% of its visible star count.
Flagship share 95%Breakout repo: annotated_deep_learning_paper_implementations
Organization pages work best when you separate portfolio breadth from flagship concentration. In labml.ai's case, the visible top three repositories account for about 99% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.
The dominant language mix here is Python (4), TypeScript (4), Jupyter Notebook (1). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.
Top Repositories
| # | Repository | Language | Stars |
|---|---|---|---|
| 1 | labmlai/annotated_deep_learning_paper_implementations 🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠 | Python | 67.4K |
| 2 | labmlai/labml 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱 | Python | 2.3K |
| 3 | labmlai/inspectus LLM Analytics | TypeScript | 713 |
| 4 | labmlai/python_autocomplete Use Transformers and LSTMs to learn Python source code | Jupyter Notebook | 197 |
| 5 | labmlai/app Moved to https://github.com/labmlai/labml/tree/master/app | TypeScript | 164 |
| 6 | labmlai/neox Simple Annotated implementation of GPT-NeoX in PyTorch | Python | 111 |
| 7 | labmlai/chrome-extension Source code of the papers.labml.ai Chrome extension | TypeScript | 79 |
| 8 | labmlai/labml.ai Our website | HTML | 21 |
| 9 | labmlai/dashboard Experiments dashboard for LabML | TypeScript | 17 |
| 10 | labmlai/db Minimalistic Object-Relational Mapper for JSON/YAML/Pickle file based db | Python | 7 |
| 11 | labmlai/research | Makefile | 1 |
| 12 | labmlai/app_cordova Cordova wrapper for labml web app | CSS | 0 |
Next step after the organization read
Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
How to read this organization snapshot
Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.
For broader background on GitStar's ranking logic and editorial guidance, see Methodology & Editorial Standards.