DEDUPED Hot AI repos, turned into use cases Get 5 a day

How to add a new model recipe to the repo with Kaggle Tpu Lab

Create a model folder with a README, kernel script and notebook following the repo's layout.

Who it is forA developer who wants to serve a different open model on Kaggle TPU and share the recipe in the same format.

The problemEach new model needs its own serving script, notebook and config, and it is easy to make them disagree.

The outcomeA new top-level model folder with README, kernel/, and notebook/ that the launcher and notebook can both use.

What you need first

  • A Kaggle account with TPU access, verified under Settings (phone number, and for some accounts a Persona identity check)
  • Kaggle's free TPU quota, around 20 TPU hours a week
  • For the terminal route: Python 3.9+ and the Kaggle CLI

Steps

  1. Read the Adding a model section of the README and the folder READMEs for Qwen3.8-27B and GLM-5.3-Flash.
  2. Create one top-level folder named after the model, with a README.md that states the numbers and how they were measured.
  3. Add a kernel/ folder with the serving script, and any patch or engine the recipe needs.
  4. Add a notebook/ folder with a run-all notebook generated from the kernel script.
  5. Keep the launcher and notebook on the same config block so one setting means the same thing in both.

Prompt for your agent

In the ARahim3/kaggle-tpu-lab repo, add a new top-level folder for <model> with README.md, kernel/ and notebook/ following the Adding a model section and the existing folders. Expected output: a new folder whose notebook runs on Kaggle TPU and serves the model. Verify the config block matches between the launcher and the notebook.

Caveat. The README says model weights keep their own licenses, so check the license of any new model before publishing a recipe.

Share: X LinkedIn Hacker News Email Copy link

Disclaimer

Deduped lists open-source repositories that belong to other people. We do not own, maintain, audit, or vouch for any of them, and a repo appearing here is not an endorsement or a safety check. The use cases, steps, and prompts are written by AI and may be incomplete, outdated, or just wrong. Where a use case is marked "Seen in the wild", we link to public posts where someone described something similar, but we have not verified those posts and the use case itself is still our AI's own write-up. Use all of this at your own risk. Only run code, commands, or agents on systems and accounts you are authorized to use, and read the code and every command before you run it, especially anything that touches credentials, files, or the network. Nothing here is professional, security, legal, or financial advice. All trademarks and project names belong to their respective owners, and their use here does not imply affiliation. Content is provided as is, without warranties of any kind. Found a mistake, a safety problem, or something you want removed? Email [email protected] and we will look at it promptly. Full disclaimer page