peft-fine-tuning

Fine-tune a large language model on a consumer GPU with limited memory using PEFT methods like LoRA and QLoRA. These techniques train less than 1% of parameters, drastically reducing compute needs for 7B-70B models.

LoRA and QLoRA are parameter-efficient fine-tuning methods that adapt large language models by training small adapter modules. QLoRA further reduces memory constraints by quantizing the base model, enabling 7B-70B parameter training on consumer hardware.

Yes, you can use QLoRA to train a 70B parameter model on consumer hardware. QLoRA quantizes the base model to minimize memory constraints, allowing parameter-efficient fine-tuning across diverse tasks on standard GPUs.

To run a PEFT adapter workflow, you need Python packages peft>=0.13.0, transformers>=4.45.0, torch>=2.0.0, and bitsandbytes>=0.43.0. You also need a properly prepared dataset and base model path to operate.

Merge multiple adapters after fine-tuning a base model using the integrated adapter-merge workflows. This enables rapid task specialization and deployment by combining trained LoRA or QLoRA weights back into the original model.

Use parameter-efficient fine-tuning instead of full model training when facing memory constraints or needing fast experimentation. PEFT reduces memory and compute needs by training less than 1% of parameters, making it ideal for limited hardware.