Mimir: A Hands-On Learning Project for Building Large Language Models from Scratch

Mimir is an educational LLM implementation project based on Sebastian Raschka's book 'Large Language Model' and its accompanying Jupyter courses. It aims to help learners gradually build a complete large language model starting from the most basic Tokenizer component, enabling them to deeply understand the underlying principles of LLMs.

Sebastian Raschka is a well-known expert in the field of machine learning. His book 'Large Language Model' systematically introduces core LLM concepts (Tokenization, Embedding, Transformer architecture, etc.). The Mimir project translates the theoretical content of the book into runnable code, providing a practical platform for developers. For developers who want to understand the principles of LLMs, implementing components by hand allows them to experience the trade-offs in design decisions. The project adopts a progressive learning path to help master core skills.

The current core implementation of Mimir is the Tokenizer module, which is responsible for converting raw text into numerical sequences that the model can process. Its key functions include:

1. Text preprocessing: Split text using regular expressions, handle punctuation, spaces, and special characters;
2. Vocabulary construction: Automatically build a vocabulary mapping table using sample corpora (e.g., "the-verdict.txt");
3. Encoding and decoding: Implement bidirectional functions of converting text to ID sequences and ID sequences back to text.

Mimir demonstrates good software engineering practices:

• Clear code structure, using object-oriented design to encapsulate Tokenizer logic for easy extension;
• Configured CI/CD pipeline (GitHub Actions) to automatically run tests and ensure code correctness;
• Includes unit tests for the Tokenizer to verify the correctness of encoding and decoding, reflecting the test-driven development approach.

Mimir is currently in the early stage, with the main implementation being the Tokenizer component. The future expansion roadmap includes:

1. Embedding layer: Convert Token IDs into continuous vectors;
2. Attention mechanism: Implement self-attention and multi-head attention;
3. Complete Transformer: Assemble components to implement text generation;
4. Training process: Implement data loading, loss calculation, gradient descent, and other steps.

The practical significance of Mimir is reflected in:

• Multilingual processing: The regular expression method can adapt to the word segmentation needs of different languages;
• Custom vocabulary: Helps handle professional terms or specific brand names;
• Efficiency optimization: The basic implementation provides a foundation for the subsequent introduction of efficient algorithms such as BPE and SentencePiece.

Mimir is an excellent LLM learning resource that translates theory into runnable code, helping learners deeply understand the principles of LLMs. Building components from scratch can establish a solid foundation and is more flexible than directly using ready-made frameworks. We look forward to the project's subsequent implementation of components such as Embedding and Transformer, becoming a complete educational LLM implementation resource.