Deep Dive into Large Language Model Architecture: An Analysis of the miniature-llms Project

An educational project that implements core components of modern large language models using PyTorch and JAX, helping developers understand the internal mechanisms of LLMs from scratch.

• Original Author/Maintainer: cbarkinozer
• Source Platform: GitHub
• Original Project Name: miniature-llms
• Project Link: https://github.com/cbarkinozer/miniature-llms
• Update Time: June 2026

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In today's era of rapid development of large language model (LLM) technology, most developers use these models as "black boxes"—inputting prompts and getting outputs, but knowing little about their internal working mechanisms. This state of "knowing the what but not the why" limits our ability to truly understand and optimize these powerful tools.

The miniature-llms project was created to address this issue. It is an educational open-source project that implements all core components of modern large language models from scratch using two mainstream deep learning frameworks: PyTorch and JAX. The core philosophy of the project is: "Build models at a 1/1000 scale—structures are real, losses will decrease, but don't expect inference results."

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When we read the official implementations of open-source models like GPT, Llama, or Qwen, we are faced with highly optimized production code: CUDA kernels, memory-efficient tricks, distributed training support, and various engineering optimizations. Although these codes are excellent in performance, they are like a maze for learners—the core algorithms are wrapped in layers of optimizations, making it difficult to see the essence.

miniature-llms adopts the opposite approach:

1. Purity: Each component is a "correct but unoptimized" implementation, no CUDA kernels, no memory tricks—only the core logic of the algorithm
2. Verifiability: Verify correctness by training on a miniature dataset on CPU and observing loss reduction, rather than relying on complex benchmark tests
3. Dual Framework Support: Provide implementations in both PyTorch and JAX, allowing learners to understand the expression differences of the same algorithm in different frameworks
4. Modular Design: All components follow unified dimension conventions and naming standards, which can be freely combined to build a complete model

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The project breaks down the LLM architecture into 13 core components, each with independent implementation and detailed conceptual explanations:

Tokenization is the first step in LLM text processing. The BPE algorithm builds a vocabulary by merging frequently occurring character pairs, balancing the trade-off between vocabulary size and expressive power. The project not only implements BPE but also explains in depth "why tokenize this way" and considerations in practical use.

Mapping discrete token IDs to a continuous vector space is the foundation of neural networks processing text. The project demonstrates the implementation of the embedding layer and its relationship with one-hot encoding.