Introducing LLM-RLHF-Tuning, a cutting-edge project implementing Reinforcement Learning from Human Feedback (RLHF) with an emphasis on Proximal Policy Optimization (PPO) and Deterministic Policy Optimization (DPO) algorithms. Designed to fine-tune and train the Alpaca, LLaMA, and LLaMA2 models more effectively, our project supports various configurations, including LoRA adapters for accelerated and deepspeed training. Ideal for AI researchers and developers seeking to push the boundaries of machine learning models.