LLM Reinforcement Learning Fine-Tuning DeepSeek Method GRPO

LLM Reinforcement Learning Fine-Tuning DeepSeek Method GRPO

[EN] LLM Fine-Tuning and Reinforcement Learning with SFT, LoRA, DPO, and GRPO Custom Data HuggingFace

What you'll learn

• You will grasp the core principles of Large Language Models (LLMs) and the overall structure behind their training processes.
• You will learn the differences between base models and instruct models, as well as the methods for preparing data for each.
• You’ll learn data preprocessing techniques along with essential tips, how to identify special tokens required by models, understanding data formats, and methods
• You’ll gain practical, hands-on experience and detailed knowledge of how LoRA and Data Collator work.
• You’ll gain a detailed understanding of crucial hyperparameters used in training, including their purpose and how they function.
• You’ll practically learn, in detail, how trained LoRA matrices are merged with the base model, as well as key considerations and best practices to follow during
• You’ll learn what Direct Preference Optimization (DPO) is, how it works, the expected data format, and the specific scenarios in which it’s used.
• You’ll learn key considerations when preparing data for DPO, as well as understanding how the DPO data collator functions.
• You’ll learn about the specific hyperparameters used in DPO training, their roles, and how they function.
• You’ll learn how to upload your trained model to platforms like Hugging Face and manage hyperparameters effectively after training.
• You’ll learn in detail how Group Relative Policy Optimization (GRPO), a reinforcement learning method, works, including an in-depth understanding of its learnin
• You’ll learn how to prepare data specifically for Group Relative Policy Optimization (GRPO).
• You’ll learn how to create reward functions—the most critical aspect of Group Relative Policy Optimization (GRPO)—through various practical reward function exam
• In what format should data be provided to GRPO reward functions, and how can we process this data within the functions? You’ll learn these details thoroughly.
• You’ll learn how to define rewards within functions and establish clear reward templates for GRPO.
• You’ll practically learn numerous details, such as extracting reward-worthy parts from raw responses and defining rewards based on these extracted segments.
• You’ll learn how to transform an Instruct model into one capable of generating “Chain of Thought” reasoning through GRPO (Group Relative Policy Optimization).

LLM Reinforcement Learning Fine-Tuning DeepSeek Method GRPO