LLM-FineTuning-Large-Language-Models
Discover detailed methodologies and practical techniques for fine-tuning large language models (LLMs), supported by comprehensive notebooks and video guides. This resource covers techniques such as 4-bit quantization, direct preference optimization, and custom dataset tuning for models like LLaMA, Mistral, and Mixtral. It also demonstrates the integration of tools like LangChain and the use of APIs, alongside advanced concepts including RoPE embeddings and validation log perplexity, providing diverse applications for AI project enhancement.
