MLSAKIIT / MLSAKIIT/stablediffusionlora
Cyclic Learning Rate Scheduler
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hacktoberfest
- Dominant language
- Python
- Stars
- 13
- Forks
- 14
- PR merge metrics
- No merged PRs in 30d
Description
Participants can implement a cyclic learning rate scheduler, which alternates between low and high learning rates during training. This technique can help LoRA layers escape local minima, improve convergence, and reduce overfitting.
Ensure that you've read the guidelines present in CONTRIBUTING.md as well as the CODE_OF_CONDUCT.md.
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
Read CONTRIBUTING.md and CODE_OF_CONDUCT.md first. The issue names no implementation file, test, or acceptance criteria; inspect the repository's training entry point to determine where a cyclic scheduler would belong and define how completion will be verified.
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