peft-fine-tuning

What This Skill Does

The peft-fine-tuning skill provides a streamlined interface for Parameter-Efficient Fine-Tuning (PEFT) of large language models (LLMs). By leveraging techniques like LoRA (Low-Rank Adaptation) and QLoRA, this skill enables users to adapt massive models—ranging from 7B to 70B parameters—to specific tasks or domains without the prohibitive cost of full parameter updates. Instead of retraining the entire model, which requires massive VRAM, this skill trains less than 1% of the total model weights. It integrates seamlessly with the HuggingFace ecosystem, providing a production-ready pathway to create lightweight, task-specific adapters that can be swapped in and out for multi-adapter serving.

Installation

To integrate this skill into your OpenClaw environment, use the following command:

clawhub install openclaw/skills/skills/desperado991128/peft

Additionally, ensure your environment has the necessary deep learning dependencies installed:

pip install peft transformers accelerate bitsandbytes datasets

Use Cases

• Domain Adaptation: Fine-tune a general-purpose model like Llama 3 on medical, legal, or technical documentation to improve domain-specific accuracy.
• Low-Resource Hardware: Utilize QLoRA to fine-tune high-parameter models on consumer-grade hardware (e.g., RTX 3090/4090) that would otherwise run out of memory during full fine-tuning.
• Multi-Task Deployments: Create a shared base model and deploy multiple tiny adapter files, allowing a single model instance to serve different use cases (e.g., one for summarization, one for code generation) simultaneously.
• Style Transfer: Adjust model tone and verbosity by training on a custom dataset of stylized instruction-response pairs.

Example Prompts

1. "Perform LoRA fine-tuning on meta-llama/Llama-3.1-8B using my dataset at ./my_data.json, focusing on the target modules for attention layers."
2. "I need to train a medical assistant model on my 5GB dataset; can you set up a QLoRA training job to save VRAM while maintaining quality?"
3. "Show me how to configure the LoraConfig for a task-specific adapter to improve my model's ability to follow complex chain-of-thought instructions."

Tips & Limitations

• Memory Constraints: While PEFT is efficient, 70B models still require significant VRAM even with QLoRA. Always monitor nvidia-smi during initialization.
• Hyperparameters: LoRA rank (r) has a significant impact. Start with r=8 or 16. If performance is poor, increase the rank, but note that it increases the number of trainable parameters.
• Quality Trade-offs: QLoRA introduces a slight quantization error. While usually negligible, critical high-precision applications may require full fine-tuning if compute budget allows.
• Dataset Quality: The output quality of an adapter is strictly limited by the quality of your training data. Garbage in, garbage out applies heavily here.