peft-fine-tuning
peft-fine-tuning is a Fine-tuning & training data skill for AI agents, published by OpenRaiser in the NanoResearch repository on GitHub. Skillspace rates peft-fine-tuning B (85/100) and it passed the safety scan; the rating is based on static analysis only.
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
- Grade
- B
- Quality
- 85 / 100
- Safety
- Passed
- GitHub stars
- 1,482
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 97 / 100
- Maturity
- Packaged: licensed, with a version or references
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 1 other repositories, in 2 collections
- Scope
- Vendor-specific: built around one product. HuggingFace PEFT library, LoRA and QLoRA fine-tuning
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
Also published in 1 other repo
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