peft-fine-tuning

Pass

Audited by Gen Agent Trust Hub on Sep 5, 2026

Risk Level: SAFE
Full Analysis
  • [SAFE]: The skill serves as a comprehensive educational resource for parameter-efficient fine-tuning (PEFT). It contains standard Python code examples for training and serving LLMs using the official HuggingFace peft and transformers libraries.
  • [SAFE]: All external URLs point to official, trusted documentation and source code repositories hosted by HuggingFace and arXiv.
  • [SAFE]: Dependency requirements (peft, transformers, torch, bitsandbytes) are standard, well-known libraries in the machine learning ecosystem. No suspicious or unversioned third-party packages are used.
  • [SAFE]: The documentation includes standard security best practices for fine-tuning, such as local adapter saving and memory optimization techniques. No instances of credential exposure, data exfiltration, or malicious command execution were found.
Audit Metadata
Risk Level
SAFE
Analyzed
Sep 5, 2026, 12:08 PM