Peft Fine Tuning
Analysis
This is a coherent instruction-only PEFT fine-tuning guide; the main thing to notice is that following it involves installing large ML packages and optionally building a dependency from source.
Findings (2)
Artifact-based informational review of SKILL.md, metadata, install specs, static scan signals, and capability signals. ClawScan does not execute the skill or run runtime probes.
Checks for instructions or behavior that redirect the agent, misuse tools, execute unexpected code, cascade across systems, exploit user trust, or continue outside the intended task.
dependencies: [peft>=0.13.0, transformers>=4.45.0, torch>=2.0.0, bitsandbytes>=0.43.0] ... pip install peft transformers accelerate bitsandbytes datasets
The skill directs the user to install external ML packages using minimum version ranges rather than pinned versions. This is expected for a PEFT fine-tuning guide, but it is still a supply-chain point users should verify.
git clone https://github.com/TimDettmers/bitsandbytes.git cd bitsandbytes CUDA_VERSION=118 make cuda11x pip install .
The troubleshooting guide includes an optional source build and local install path for bitsandbytes. This is purpose-aligned for CUDA troubleshooting, but source builds run code from the referenced repository.
