peft-fine-tuning
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.
Install
npx skills add davila7/claude-code-templates@peft-fine-tuningRuns in your terminal. Adds the skill globally for Claude Code, Cursor, Codex and others; add -g -y to skip prompts.
What it does
PEFT (Parameter-Efficient Fine-Tuning) Fine-tune LLMs by training <1% of parameters using LoRA, QLoRA, and 25+ adapter methods. When to use PEFT Use PEFT/LoRA when: - Fine-tuning 7B-70B models on consumer GPUs (RTX 4090, A100) - Need to train <1% parameters (6MB adapters vs 14GB full model) - Want fast iteration with multiple task-specific adapters - Deploying multiple fine-tuned variants from one base model Use QLoRA (PEFT + quantization) when: - Fine-tuning 70B models on single 24GB GPU - Memory is the primary constraint - Can accept ~5% quality trade-off vs full fine-tuning Use full…
Excerpt from the skill's own SKILL.md. Read the full file on GitHub before installing: skills run with your agent's permissions.
View source on GitHubBefore you install
Skills are plain text instructions the agent follows, sometimes with scripts. Check the source, prefer repositories with many installs and stars, and read any script it ships.
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