A high-performance library for fine-tuning Large Language Models by training less than 1% of parameters using methods like LoRA and QLoRA.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install peft
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install peft using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is PEFT: Parameter-Efficient Fine-Tuning?
PEFT (Parameter-Efficient Fine-Tuning) is a specialized library designed to make Large Language Model (LLM) adaptation accessible and efficient. By focusing on training a tiny fraction of a model's total parameters, PEFT minimizes the hardware requirements typically associated with deep learning. This approach allows developers to utilize Openclaw Skills to adapt state-of-the-art models like Llama 3.1 and Mistral on hardware as accessible as a single consumer GPU.
The library is natively integrated with the HuggingFace transformers ecosystem, supporting over 25 different fine-tuning methods including LoRA, QLoRA, and IA3. This makes it an essential tool for developers who need to create specialized AI agents or task-specific models without the overhead of full-parameter updates, significantly reducing storage costs from gigabytes to mere megabytes per adapter.
PEFT: Parameter-Efficient Fine-Tuning Use Cases
- Adapting 7B to 70B parameter models on consumer-grade hardware like the RTX 4090.
- Deploying multi-tenant AI services where one base model serves dozens of task-specific adapters.
- Fine-tuning models in memory-constrained environments using 4-bit quantization (QLoRA).
- Rapid prototyping and iteration of domain-specific LLMs with minimal storage overhead.
How PEFT: Parameter-Efficient Fine-Tuning Works
- Load a pre-trained base model from the HuggingFace Hub, optionally applying 4-bit or 8-bit quantization to save VRAM.
- Configure the PEFT method by defining a LoraConfig or similar object to specify target layers and rank.
- Wrap the base model using the get_peft_model function, which freezes original weights and injects trainable adapter modules.
- Conduct training using standard optimization libraries like Accelerate or the Trainer API.
- Save and export the lightweight adapter weights (often <100MB) for inference or distribution.
PEFT: Parameter-Efficient Fine-Tuning Setup
# Basic installation of the PEFT library
pip install peft
# Recommended installation with quantization support
pip install peft bitsandbytes
# Complete environment for LLM fine-tuning
pip install peft transformers accelerate bitsandbytes datasets
PEFT: Parameter-Efficient Fine-Tuning Data Schema & Taxonomy
The skill manages model data through a decoupled architecture of base weights and adapter modules. The following table describes the primary data components:
| Component | Description | Typical Size |
|---|---|---|
| Base Model | The static, frozen pre-trained weights (e.g., Llama-3) | 14GB - 140GB |
| Adapter Weights | The trainable parameters (e.g., adapter_model.bin) | 6MB - 100MB |
| Config File | Metadata defining the PEFT architecture (adapter_config.json) | <1KB |
| Merged Model | The result of fusing adapters into the base weights for deployment | Same as Base |
PEFT: Parameter-Efficient Fine-Tuning Advanced Features
- Multi-adapter serving allows dynamic switching between different fine-tuned tasks at runtime without reloading the model.
- QLoRA support enables training 70B models on a single 24GB GPU by utilizing 4-bit NormalFloat quantization.
- Automatic rank selection via AdaLoRA optimizes parameter allocation based on importance scores during training.
- Integration with vLLM and TRL (SFTTrainer) provides a production-ready path for both training and high-throughput inference using Openclaw Skills.
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