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.
npx skills add OpenRaiser/NanoResearch --skill peft --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 fine-tuning instead when:** - Training small models (<1B parameters) - Need maximum quality and have compute budget - Significant domain shift requires updating all weights ## Quick start ### Installation ```bash # Basic installation pip install peft # With quantization support (recommended) pip install peft bitsandbytes # Full stack pip install peft transformers accelerate bitsandbytes datasets ``` ### LoRA fine-tuning (standard) ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer from peft import get_peft_model, LoraConfig, TaskType from
- When to use PEFT
- Quick start
- Installation
- LoRA fine-tuning (standard)
- QLoRA fine-tuning (memory-efficient)
- LoRA parameter selection
- Rank (r) - capacity vs efficiency
- Alpha (loraalpha) - scaling factor
- Target modules by architecture
- Loading and merging adapters
- Load trained adapter
- Merge adapter into base model
- Multi-adapter serving
- PEFT methods comparison
Basic installation pip install peft With quantization support (recommended) pip install peft bitsandbytes Full stack pip install peft transformers accelerate bitsandbytes datasets
What does the peft-fine-tuning skill do?
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.
How do I install it?
Run `npx skills add OpenRaiser/NanoResearch --skill peft --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From OpenRaiser/NanoResearch, a repository with 1,480 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.