fine-tuning
fine-tuning is a Fine-tuning & training data skill for AI agents, published by ProfSynapse in the Synaptic-Tuner repository on GitHub. Skillspace rates fine-tuning D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Complete reference for the fine-tuning pipeline (SFT, KTO, GRPO), cloud HF Jobs workflows, autonomous experiment search, checkpoint evaluation, and LoRA surgery. Covers training CLI flags, YAML configuration, model presets, dataset requirements, LoRA settings, training monitoring, hyperparameter search, and post-training optimization. Use when training models, configuring training runs, choosing hyperparameters, running cloud experiments, inspecting HF jobs, or troubleshooting training issues. This skill is about USING the training system via CLI and YAML — never modifying source code.
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 26
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
- 85 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- Low-risk patterns
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
What the scanner flagged
- mentions secrets in text
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
Skillspace indexes and rates this skill; it does not host or endorse it. Run third-party skills at your own risk.