peft-fine-tuning

You can fine-tune an LLM with limited GPU memory using parameter-efficient fine-tuning methods like LoRA and QLoRA. These methods train only a fraction of the model's parameters, minimizing accuracy loss while enabling training on consumer GPUs with restricted memory.

LoRA and QLoRA are both parameter-efficient fine-tuning methods for LLMs. QLoRA further optimizes memory usage by quantizing the base model, allowing fine-tuning on even more restricted GPU hardware compared to standard LoRA, while minimizing accuracy loss.

Yes, you can serve multiple fine-tuned models from a single base model using multi-adapter serving. This approach allows you to load and switch between different LoRA or QLoRA adapters with minimal overhead, eliminating the need to load multiple full model copies.

Yes, implementing parameter-efficient fine-tuning requires specific libraries including peft, transformers, torch, and bitsandbytes. You also need compatible hardware configurations to effectively run these PEFT methods and optimize your LLM training process.

Parameter-efficient fine-tuning aims to minimize accuracy loss while training only a fraction of the model's parameters. By using methods like LoRA and QLoRA, you can achieve performance close to full fine-tuning while significantly reducing memory requirements.

The best way to start with PEFT is to install the peft library with pip and follow the provided scripts and references. These resources guide you through setting up LoRA or QLoRA configurations to optimize your LLM fine-tuning on limited hardware.