local-llm-fine-tuning
Pass
Audited by Gen Agent Trust Hub on Sep 27, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill consists of educational documentation and boilerplate Python code for converting and validating datasets. It does not contain any malicious patterns such as obfuscation, credential theft, or unauthorized persistence mechanisms.\n- [EXTERNAL_DOWNLOADS]: The documentation references and utilizes well-known machine learning libraries (Hugging Face Transformers, PEFT, bitsandbytes) and models (Meta Llama 3) from trusted sources and community repositories.\n- [INDIRECT_PROMPT_INJECTION]: The skill includes scripts for processing external data files into model training formats, which constitutes a standard data ingestion surface.\n
- Ingestion points: The scripts
csv_to_alpaca,text_to_completion, andvalidate_jsonlinreferences/dataset-formats.mdread from local CSV and text files.\n - Boundary markers: Absent in
references/dataset-formats.md; the scripts convert raw data without injecting specific instruction-isolation delimiters.\n - Capability inventory: The provided scripts in
references/dataset-formats.mdare limited to file I/O and JSON processing; no high-risk capabilities like network operations or arbitrary command execution are utilized.\n - Sanitization: Absent in
references/dataset-formats.md; the scripts perform format conversion without content-level sanitization, which is expected for the intended use-case of preparing training data.
Audit Metadata