ml-explore / ml-explore/mlx-examples
[Feature Request] LLaVA 1.6 LoRA fine-tuning example
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- Dominant language
- Python
- Stars
- 9k
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Description
Building on the amazing work by @mzbac and @nkasmanoff in https://github.com/ml-explore/mlx-examples/pull/461, I'd really love an example of how LLaVA 1.6 (aka llava next) can be fine-tuned with a LoRA.
I might be able to make progress on this myself, but it'll take me some time. Any help or thoughts on how to best approach this would be appreciated. (Especially from @mzbac and/or @nkasmanoff.)
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
Start by reading the work referenced in pull request #461 and identify how the existing LLaVA 1.6 example is structured. Determine the entry point for adding LoRA fine-tuning support, then verify that the repository contains a usable LLaVA 1.6 LoRA fine-tuning example.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- ai, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 48/100