peft-fine-tuning

Fine-tune large language models on memory-constrained GPUs by training small adapters instead of full models using PEFT methods like LoRA and QLoRA. This skill supports 7B–70B parameter models on limited hardware.

Parameter-efficient fine-tuning with LoRA, QLoRA, and AdaLoRA involves training small adapters rather than full models to reduce memory usage. This skill covers all three techniques for efficient adaptation of 7B–70B models.

Multi-adapter deployment is supported, allowing you to deploy task-specific variants using a single base model. This skill enables configuring and serving multiple PEFT adapters for different downstream tasks.

PEFT fine-tuning integrates seamlessly with the HuggingFace transformers ecosystem for rapid experimentation. It requires transformers>=4.45.0, torch>=2.0.0, and PEFT>=0.13.0, with optional bitsandbytes for quantization.

Set up a lightweight training workflow for 7B–70B models by selecting a base model, choosing PEFT adapters like LoRA or AdaLoRA, and running the training process. This skill supports Linux, macOS, and Windows environments.

Use QLoRA for fine-tuning when GPU memory is heavily constrained, as it quantizes the base model to reduce memory requirements further than standard LoRA. This skill supports both methods for efficient large model adaptation.