PEFT

🤗 PEFT, or Parameter-Efficient Fine-Tuning (PEFT), is a library for efficiently adapting pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model’s parameters. PEFT methods only fine-tune a small number of (extra) model parameters, significantly decreasing computational and storage costs because fine-tuning large-scale PLMs is prohibitively costly. Recent state-of-the-art PEFT techniques achieve performance comparable to that of full fine-tuning.

PEFT is seamlessly integrated with 🤗 Accelerate for large-scale models leveraging DeepSpeed and Big Model Inference.

Supported methods include:

1. LoRA: LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS
2. Prefix Tuning: Prefix-Tuning: Optimizing Continuous Prompts for Generation, P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
3. P-Tuning: GPT Understands, Too
4. Prompt Tuning: The Power of Scale for Parameter-Efficient Prompt Tuning