{
 "schema_version": 1,
 "generated": "2026-10-20",
 "id": "jrz97619761-test-model-thing--train-own-byte-level-model-on-mlx",
 "repo": "jrz97619761/test-model-thing",
 "repo_url": "https://github.com/jrz97619761/test-model-thing",
 "repo_slug": "jrz97619761-test-model-thing",
 "title": "Train a small byte-level model on your own machine",
 "page_title": "How to train a small byte-level model on your own machine with Test Model Thing",
 "summary": "Install MLX, train the 4.5M-parameter proof of concept, and save weights locally.",
 "who": "A developer or student with an Apple Silicon Mac or a Linux machine who wants to see a small experimental language model train from scratch.",
 "problem": "Model weights are not included in the repo, so you need to train your own before anything can run.",
 "outcome": "You have a locally trained model whose weights are saved to disk, ready for chat or benchmarking.",
 "steps": [
  "Create and activate a venv in the repo folder.",
  "Install MLX: on Mac run `pip install mlx`, on Linux with GPU run `pip install mlx[cuda]`, on Linux with CPU run `pip install mlx[cpu]`.",
  "Start training with `python main.py   train`.",
  "Let it run, or stop it with ^C; the README says weights are saved on exit and periodically during training.",
  "Confirm that weight files were written to disk before moving on."
 ],
 "agent_prompt": "In the jrz97619761/test-model-thing repo, set up a venv, install MLX for this machine, and run `python main.py   train` for about 5 minutes. Report the time taken and confirm that saved weights exist on disk.",
 "agents": [
  "any"
 ],
 "difficulty": "Intermediate",
 "time": "1 hour",
 "basis": "readme",
 "caveat": "The README calls this a proof of concept that is not a finished work, and the dataset must be configured yourself for dataset mode.",
 "category": "Research",
 "tags": [
  "Research"
 ],
 "lang": "python",
 "requirements": [
  "Python with a virtual environment (the README says to initialize a venv)",
  "Apple Silicon Mac, or Linux with a GPU or CPU, or WSL (the README says it trains on WSL)",
  "A training dataset, since model weights are not provided and dataset mode must be configured by you",
  "Time for training, since the README's examples used 5 minutes to about 12 hours"
 ],
 "risk": "none",
 "seen_in_the_wild": false,
 "mentioned_publicly": false,
 "risks": [],
 "best": true,
 "score": 1.0,
 "evidence": [],
 "url": "https://dedupedai.com/use-cases/jrz97619761-test-model-thing/train-own-byte-level-model-on-mlx/",
 "md": "https://dedupedai.com/use-cases/jrz97619761-test-model-thing/train-own-byte-level-model-on-mlx/index.md",
 "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 hello@dedupedai.com and we will look at it promptly.",
 "api": "https://dedupedai.com/api/v1/usecases/jrz97619761-test-model-thing--train-own-byte-level-model-on-mlx.json",
 "api_version": "v1"
}