Large language models (LLMs) such as ChatGPT are created through intricate processes involving data preparation, model training, and fine-tuning. A new course by freeCodeCamp offers learners an opportunity to develop their own language model from scratch. The course uses illustrative examples, including Moroccan Darija, to make the learning experience effective and relatable.
The tutorial is structured to guide users through every aspect necessary to train an LLM. It begins with fundamental concepts and small datasets to help learners grasp the basics. As the course progresses, it scales up to larger datasets. This methodical approach ensures that learners can build on their knowledge incrementally.
Key topics covered in the course include tokenization, an essential step for converting text into a format that a computer model can process. It delves into Transformer architecture, a powerful model structure that has become foundational in natural language processing. Additionally, the course covers pre-training and fine-tuning techniques like Low-Rank Adaptation (LoRA), which allow the model to perform specific tasks more efficiently.
One unique aspect of the course is its practical application to real-world data. For instance, learners will explore how to use chat data from platforms like WhatsApp or Telegram. The process involves data extraction, cleaning, and encoding text using byte pair encoding. The final steps involve building the Transformer model and fine-tuning it to develop conversational AI assistants or to mimic someone’s unique communication style. This approach also extends to developing models for underrepresented languages.
To facilitate the learning process, the course includes extensive resources, such as slides, notebooks, and code examples. By the end of the course, learners will possess a comprehensive understanding and the skills needed to create their own functional, fine-tuned language models.

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A new comprehensive course by freeCodeCamp teaches learners how to develop their own large language models (LLMs) from scratch. The course covers fundamental concepts, tokenization, Transformer architecture, and fine-tuning techniques like Low-Rank Adaptation (LoRA). Practical applications include working with chat data and developing models for underrepresented languages. Extensive resources provided include slides, notebooks, and code examples.

Train Your Own Large Language Model: A Comprehensive Course

Anytime someone calls ChatGPT a “LLM”, immediately invalidates the perspective for me. It’s an app that gives you access to a variety of models and tools

Well this sounds quite interesting.