peft-fine-tuning

Fine-tune large language models with limited GPU memory by using parameter-efficient fine-tuning methods like LoRA and QLoRA. These techniques train less than 1% of the model's parameters, significantly reducing memory requirements on consumer hardware.

Yes, you can fine-tune 7B to 70B parameter models on consumer GPUs using QLoRA and other parameter-efficient methods. This approach is specifically optimized for memory-constrained environments, allowing large model training without massive VRAM.

LoRA and QLoRA are parameter-efficient fine-tuning methods that train under 1% of model parameters. QLoRA further reduces memory usage by quantizing the base model, making it ideal for more severe GPU memory constraints during fine-tuning.

Multi-adapter serving is supported by deploying multiple fine-tuned adapters simultaneously. This Skill utilizes parameter-efficient methods to train distinct adapters, allowing you to serve multiple fine-tuned configurations from a single base model.

Yes, bitsandbytes is a required dependency for performing QLoRA fine-tuning. You also need the peft and transformers libraries to implement parameter-efficient training and manage the large language model architecture.

Parameter-efficient fine-tuning is the best way to train less than 1% of model parameters. This Skill supports 25+ adapter methods, enabling you to update a small subset of weights while maintaining model accuracy and saving memory.