The scaling of Large Language Models (LLMs) for retrieval-based tasks, particularly in Retrieval Augmented Generation (RAG), faces significant memory constraints, especially when fine-tuning extensive prompt sequences. Current open-source libraries support full-model inference and fine-tuning across multiple GPUs but fall short of accommodating the efficient parameter distribution required for retrieved context. Addressing this gap, we introduce a novel framework for PEFT-compatible fine-tuning of Llama-2 models, leveraging distributed training. Our framework uniquely utilizes JAX's just-in-time (JIT) compilation and tensor-sharding for efficient resource management, thereby enabling accelerated fine-tuning with reduced memory requirements. This advancement significantly improves the scalability and feasibility of fine-tuning LLMs for complex RAG applications, even on systems with limited GPU resources. Our experiments show more than 12x improvement in runtime compared to Hugging Face/DeepSpeed implementation with four GPUs while consuming less than half the VRAM per GPU. Our library will be open-sourced in due course.

通过使用分布式训练，借助JAX的即时编译（JIT）和张量分片，我们引入了一种新的PEFT兼容的Llama-2模型微调框架，以有效管理资源，从而实现了加速微调并减少内存需求，从而显着改善了用于复杂RAG应用的LLM的可扩展性和可行性。

JORA：JAX 张量并行 LoRA 检索增强微调库