harleyszhang/llm_note

LLM notes, including model inference, transformer model structure, and llm framework code analysis notes.

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Emerging

This resource provides comprehensive notes and a course on building and optimizing large language model (LLM) inference frameworks. It covers everything from transformer model structures and LLM quantization to advanced inference optimization and high-performance computing using technologies like Triton and CUDA. The ideal user is a machine learning engineer or researcher focused on deploying and speeding up LLM applications.

866 stars.

Use this if you are a machine learning engineer or researcher looking to deeply understand and implement efficient, high-performance inference for large language models.

Not ideal if you are a data scientist or developer primarily interested in using existing LLM APIs or off-the-shelf libraries without delving into the underlying optimization details.

LLM deployment model optimization high-performance computing AI infrastructure deep learning engineering
No License No Package No Dependents
Maintenance 6 / 25
Adoption 10 / 25
Maturity 8 / 25
Community 18 / 25

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Stars

866

Forks

87

Language

Python

License

Last pushed

Dec 10, 2025

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