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How to train a small byte-level model on your own machine with Test Model Thing

Install MLX, train the 4.5M-parameter proof of concept, and save weights locally.

  • Top pick
  • Research
  • Intermediate
  • 1 hour
  • From the README
  • Any agent

Who it is forA developer or student with an Apple Silicon Mac or a Linux machine who wants to see a small experimental language model train from scratch.

The problemModel weights are not included in the repo, so you need to train your own before anything can run.

The outcomeYou have a locally trained model whose weights are saved to disk, ready for chat or benchmarking.

What you need first

  • 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

Steps

  1. Create and activate a venv in the repo folder.
  2. 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].
  3. Start training with python main.py train.
  4. Let it run, or stop it with ^C; the README says weights are saved on exit and periodically during training.
  5. Confirm that weight files were written to disk before moving on.

Prompt for your agent

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.

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.

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