MLX Text-completion Finetuning Notebook

Notice! For those looking for a more in-depth Jupyter Notebook for fine-tuning, see this one published by the lead author of MLX.

A simple Jupyter Notebook for doing text-completion fine-tuning using QLoRA in the MLX framework. By default this Notebook fine-tunes TinyLlama 1.1b on 500 entries of the TinyStories dataset. It should be useable by any Apple Silicon MacBook with at least 16GB RAM.

Brought to you by The Curriculum Protocol squad!

Quickstart | Roadmap

Quickstart

Install

1. Follow these instructions to install Jupyter Lab.
2. Download this repo (or just the .ipynb file and the requirements.txt) to a folder on your Mac.
3. Right click on that folder - i.e. the one containing your files - and select Open in Terminal.
4. (Optional) Install Virtualenv to set up a virtual environment before installing the requirements.txt.
5. (Optional) In the terminal, type virtualenv venv and then source venv/bin/activate to activate the virtual env.
6. Run the command: pip3 install -r requirements.txt.
7. In the terminal, type jupyter lab. The UI should open automatically - if it doesn't, there should be a URL in the terminal that you can copy and paste into your browser.
8. Once the UI is up, open up the .ipynb file (i.e. the Notebook).

Running the Notebook

1. Instructions are in the Notebook! In short, simply click Run in the top left. There should be a drop down - select Run All Cells.
2. Once you've run all cells, you've officially fine-tuned a model in MLX!
3. (Optional) Swap out the dataset for your own dataset.

Roadmap - Nothing for now

EDIT: Unfortunately my priorities have been elsewhere, so for now I'm not continuing with the maintenance of this notebook. Apologies!

• As the fantastic team behind MLX continues to work on bringing more features to MLX-lm and different loss functions become possible, we'll release more Notebooks (such as for Instruction-tuning, DPO, PPO, etc.)
• Additionally, it's currently possible to target the MLP weights and train at much higher rank in MLX QLoRA, but it requires manually editing a file in the MLX-lm QLoRA tuner directory - which is a bit faffy. Once that becomes editable via config we'll release an updated Notebook.
• The mlx-tuning-fork repo offers a few cool features - namely learning rate scheduling. Would be good to release an updated Notebook that makes use of the scheduler.
• Dataset building with no coding knowledge is very difficult. Would be great to release a Notebook to explain process of creating a dataset in more accessible terms. Or one that abstracts away complexity of doing dataset curation. Or both. The Augmentoolkit API fork could be good starting point.
• Most (accessible) serving software is .GGUF-based. But you have to fuse weights to convert to .GGUF; this can lead to performance trade-offs. Would be good to build another notebook to train and host in MLX-server to help people remain in the MLX ecosystem.

Citing MLX

The MLX software suite was initially developed with equal contribution by Awni Hannun, Jagrit Digani, Angelos Katharopoulos, and Ronan Collobert. If you find MLX useful in your research and wish to cite it, please use the following BibTex entry:

@software{mlx2023,
  author = {Awni Hannun and Jagrit Digani and Angelos Katharopoulos and Ronan Collobert},
  title = {{MLX}: Efficient and flexible machine learning on Apple silicon},
  url = {https://github.com/ml-explore},
  version = {0.0},
  year = {2023},
}