What you’ll learn
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You will grasp the core principles of Large Language Models (LLMs) and the overall structure behind their training processes.
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You will learn the differences between base models and instruct models, as well as the methods for preparing data for each.
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You’ll learn data preprocessing techniques along with essential tips, how to identify special tokens required by models, and understand data formats and methods
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You’ll gain practical, hands-on experience and detailed knowledge of how LoRA and Data Collator work.
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You’ll gain a detailed understanding of crucial hyperparameters used in training, including their purpose and how they function.
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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
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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.
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You’ll learn key considerations when preparing data for DPO, as well as understanding how the DPO data collator functions.
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You’ll learn about the specific hyperparameters used in DPO training, their roles, and how they function.
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You’ll learn how to upload your trained model to platforms like Hugging Face and manage hyperparameters effectively after training.
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You’ll learn in detail how Group Relative Policy Optimization (GRPO), a reinforcement learning method, works, including an in-depth understanding of its learning
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You’ll learn how to prepare data specifically for Group Relative Policy Optimization (GRPO).
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You’ll learn how to create reward functions—the most critical aspect of Group Relative Policy Optimization (GRPO)—through various practical reward functionexamplesm
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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.
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You’ll learn how to define rewards within functions and establish clear reward templates for GRPO.
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You’ll practically learn numerous details, such as extracting reward-worthy parts from raw responses and defining rewards based on these extracted segments.
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You’ll learn how to transform an Instruct model into one capable of generating “Chain of Thought” reasoning through GRPO (Group Relative Policy Optimization).
Requirements
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Basic knowledge of Python programming.
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Introductory-level familiarity with artificial intelligence and machine learning concepts.
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Ideally, prior experience with Jupyter Notebook or Google Colab.
Description
In this course, you will step into the world of Large Language Models (LLMs) and learn both fundamental and advanced end-to-end optimization methods. You’ll begin with the SFT (Supervised Fine-Tuning) approach, where you’ll discover how to properly prepare your data and create customized datasets using tokenizers and data collators through practical examples. During the SFT process, you’ll learn the key techniques for making large models lighter and more efficient with LoRA (Low-Rank Adaptation) and quantization, and explore step by step how to integrate them into your projects.
After solidifying the basics of SFT, we will move on to DPO (Direct Preference Optimization). DPO allows you to obtain user-focused results by directly reflecting user feedback in the model. You’ll learn how to format your data for this method, how to design a reward mechanism, and how to share models trained on popular platforms such as Hugging Face. Additionally, you’ll gain a deeper understanding of how data collators work in DPO processes, learning practical techniques for preparing and transforming datasets in various scenarios.
The most significant phase of the course is GRPO (Group Relative Policy Optimization), which has been gaining popularity for producing strong results. With GRPO, you will learn methods to optimize model behavior not only at the individual level but also within communities or across different user groups. This makes it more systematic and effective for large language models to serve diverse audiences or purposes. In this course, you’ll learn the fundamental principles of GRPO and then solidify your knowledge by applying this technique with real-world datasets.
Throughout the training, we will cover key topics—LoRA, quantization, SFT, DPO, and especially GRPO—together, supporting each topic with project-oriented applications. By the end of this course, you will be fully equipped to manage every stage with confidence, from end-to-end data preparation to fine-tuning and group-based policy optimization. Developing modern and competitive LLM solutions that focus on both performance and user satisfaction in your own projects will become much easier.
Who this course is for:
- Who is this course for? Data scientists and ML engineers who want to specialize in Large Language Model (LLM) training techniques.
- Individuals who want to master essential tips and best practices for data preparation.
- AI developers are aiming to build their own customized language models.
- Individuals who want hands-on experience with advanced techniques like Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO).
- Individuals who want practical, hands-on experience with the Group Relative Policy Optimization (GRPO) technique.
- Individuals who want to learn essential tips for data preparation and adapt their own custom datasets for language models.
- Those interested in reinforcement learning methods and optimizing models based on user feedback.
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