Beyond LoRA: Can you beat the most popular fine-tuning technique?
Researchers at Hugging Face compared popular LLM fine-tuning methods, finding that Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA outperform full model fine-tuning on many tasks. The study evaluated methods like BitFit, LoRA, and Adapter tuning on GLM-130B, Llama-2-7B, and OPT-1.3B models. You can explore the code and results on the Hugging Face blog.
Key takeaways
- PEFT methods like LoRA beat full fine-tuning on many tasks.
- Researchers tested methods on GLM-130B, Llama-2-7B, and OPT-1.3B models.
- Code and results are available on Hugging Face blog.
Researchers at Hugging Face compared popular LLM fine-tuning methods, finding that Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA outperform full model fine-tuning on many tasks. The study evaluated methods like BitFit, LoRA, and Adapter tuning on GLM-130B, Llama-2-7B, and OPT-1.3B models. You can explore the code and results on the Hugging Face blog.
Key takeaways
- PEFT methods like LoRA beat full fine-tuning on many tasks.
- Researchers tested methods on GLM-130B, Llama-2-7B, and OPT-1.3B models.
- Code and results are available on Hugging Face blog.