Benchmarking

Training and Finetuning of LLMs

Finetuning frameworks like LoRA (Low-Rank Adaptation) and QLoRA (Quantized Low-Rank Adaptation) revolutionize the way large language models (LLMs) are adapted for specific tasks, offering efficiency and scalability. These methods modify only a fraction of a model’s parameters during fine-tuning, reducing resource requirements while maintaining or enhancing model performance. LoRA introduces low-rank matrices to efficiently adjust large models without retraining their entire architecture, while QLoRA combines this approach with quantized precision to further optimize memory and computation overhead. These frameworks make high-quality fine-tuning accessible for resource-constrained environments, broadening the adoption of LLMs.