I built a large language model "from scratch"
Brett Fitzgerald built a large language model inspired by Sebastian Raschka's book, emphasizing hands-on coding, tokenization, fine-tuning for tasks, and the importance of debugging and physical books for learning.
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Brett Fitzgerald shares his experience of building a large language model (LLM) from scratch, inspired by Sebastian Raschka's book on the subject. He emphasizes the importance of hands-on coding, opting to type out all code samples to enhance his understanding. Fitzgerald details the process of tokenization, where text is converted into numerical tokens, and how these tokens are used to train the model by establishing relationships based on their context in the training data. He describes the model's text generation capabilities, where it predicts the next word based on previous tokens. Fitzgerald also discusses the fine-tuning process, which involves training the model on specific tasks, such as spam classification. He reflects on his learning journey, noting that while he gained significant insights, he struggled with retaining some concepts. He recognizes the value of debugging as a learning tool and expresses a preference for physical books over digital formats for better retention. Fitzgerald concludes by contemplating further study in AI and machine learning, while also sharing additional resources and updates from Raschka that clarify some of his earlier misunderstandings.
- Brett Fitzgerald built a large language model using Sebastian Raschka's book as a guide.
- He emphasized hands-on coding and the process of tokenization in LLM development.
- The model learns relationships between tokens to generate text based on context.
- Fine-tuning allows the model to specialize in specific tasks, like spam detection.
- Fitzgerald prefers physical books for learning and acknowledges the importance of debugging in understanding code.
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Sebastian Raschka is hosting a 3-hour workshop on building Large Language Models, covering implementation, training, and evaluation, with resources including a GitHub repository and his book on LLMs.
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Andrej Karpathy's video on large language models covers their architecture, training, and applications, emphasizing data collection, tokenization, hallucinations, and the importance of structured prompts and ongoing research for improvement.