Mini toolsDefinition
An efficient fine-tuning method combining quantization and LoRA for LLMs, reducing memory/compute needs.
Detailed Explanation
An efficient fine-tuning technique for large language models that combines quantization (reducing numerical precision, e.g., to 4-bit) with Low-Rank Adaptation (LoRA) to drastically reduce memory and computational requirements while maintaining performance.
Use Cases
Fine-tuning large language models on consumer hardware, reducing deployment costs, enabling customization of massive models with limited resources.
