ml-explore / ml-explore/mlx-examples
interesting new finetuning approach from stanford - ReFT
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- Dominant language
- Python
- Stars
- 9k
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Description
https://github.com/stanfordnlp/pyreft
uses flash attn and pyvene (https://github.com/stanfordnlp/pyvene) but don't see any specific kernels aside from flashattn. tried this on my cuda machine and it's neat - not sure how effective at scale yet, but worth exploring. anyone else looking into this?
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Begin by reviewing the linked Stanford pyreft and pyvene projects and comparing their ReFT and flash-attention integration with this repository’s Python MLX examples. The issue names no target file, entry point, test, or success criteria, so a concrete scope would need to be established before implementation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100