peft-fine-tuning

You can fine-tune Llama models on consumer hardware using parameter-efficient methods like LoRA and QLoRA to minimize GPU memory consumption. This approach trains a fraction of the parameters, allowing high-quality model updates on limited resources like a single RTX 4090.

LoRA fine-tunes models by training low-rank adapter matrices, while QLoRA further reduces memory by quantizing the base model. Both parameter-efficient methods adapt large language models for domain-specific tasks using a fraction of the memory required for full fine-tuning.

Yes, you can adapt a 70B model on a 24GB GPU using QLoRA. This memory-optimized workflow quantizes the base model and trains lightweight adapters, making it possible to fine-tune large language models on limited hardware resources.

Yes, the fine-tuning workflow supports diverse architectures including Llama, Mistral, and Falcon. You can apply parameter-efficient adaptation techniques to these models for both domain-specific adaptation and instruction tuning.

Multi-adapter support enables switching between different task-specific adapters at runtime without reloading the base model. This allows you to maintain multiple fine-tuned versions and dynamically load the appropriate adapter for your specific task.