#LLaMa3

Explore a methodology to enhance the performance of LLaMa3-8B in multiple non-English languages through advanced fine-tuning techniques and Retrieval-Augmented Generation (RAG). This guide details the step-by-step process, from dataset translation to the use of QLoRA and PEFT for efficient language model tuning. It covers a variety of foundation models, including LLaMa3 and Mistral, providing broad compatibility. Notably cost-effective, the project can be executed using free GPU resources like Google Colab. Discover the integration of various translation paradigms and implementation of DPO for improved model responses, suitable for developers enhancing multilingual chat platforms.