This repository offers a structured curriculum for understanding Large Language Models (LLMs), blending theoretical concepts with practical code implementations. Designed for engineers and researchers, it provides a deep dive into LLM mechanics, enabling users to systematically learn core theories and grasp their real-world application through code. The benefit is a robust, hands-on comprehension of LLM technology, demystifying complex architectures and their operational nuances.

How It Works

The project adopts a progressive learning approach, beginning with the foundational Transformer architecture and detailing its training and inference mechanisms. It then systematically explores diverse LLM paradigms, including Encoder-only, Encoder-Decoder, and the prevalent Decoder-only models like GPT and LLaMA series, as well as DeepSeek variants. Furthermore, it introduces cutting-edge architectures such as State Space Models (SSM) and RWKV, highlighting their distinct designs and potential advantages over traditional Transformers. This comprehensive coverage ensures a thorough, code-level understanding of how these complex models are built, trained, and deployed, facilitating practical application and further research.

Quick Start & Requirements

No installation, setup, or dependency information is available in the provided README snippet.

Highlighted Details

• In-depth coverage of the Transformer architecture, including its training and inference pipelines, forming the bedrock of modern LLMs.
• Detailed explanations of major LLM architectural families: Encoder-only (e.g., BERT), Encoder-Decoder (e.g., T5), and Decoder-only (e.g., GPT, LLaMA, DeepSeek), clarifying their distinct use cases and design philosophies.
• Introduction to contemporary architectures like State Space Models (SSM) and RWKV, highlighting their distinct approaches to sequence modeling and potential efficiency gains.
• Methodologies for evaluating LLM performance are systematically presented, crucial for benchmarking and model selection.

Maintenance & Community

No details regarding maintenance, community channels, or contributors are present in the provided README snippet.

Licensing & Compatibility

No licensing information or compatibility notes are available in the provided README snippet.

Limitations & Caveats

The provided README snippet outlines the curriculum and does not specify any known limitations, alpha status, or unsupported platforms.