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A minimalist, single-file implementation of Llama 2 inference in pure C, designed for simplicity and educational purposes.
This project is a concise, bare-bones implementation of the Llama 2 inference process, written entirely in pure C. Its primary goal is to make LLM inference accessible, understandable, and easy to experiment with, all within a single source file.
Existing large language model (LLM) frameworks are often complex, involve multiple dependencies, and can be challenging to understand from scratch. This project provides a simple, self-contained reference implementation to demystify LLM inference for educational and experimental purposes.
Entire inference code contained within a single .c file for maximum simplicity and portability.
Written exclusively in pure C (C99 standard), with no external dependencies beyond standard libraries.
Focuses on clarity and readability to serve as an educational tool for understanding LLM inference.
Includes minimal necessary components for loading weights and running inference.
The simplicity and pure C nature of this project make it suitable for various learning, experimentation, and integration scenarios:
Use the code as a reference to understand the forward pass computation, token sampling, and weight loading process of Llama 2.
Provides a clear, executable example for educational purposes, supplementing theoretical knowledge.
Integrate the core inference logic into C/C++ projects or embed it on devices with limited resources where complex ML frameworks are not feasible.
Enables deploying LLM capabilities in new environments due to its minimal footprint and lack of external dependencies.
Modify and experiment with the inference process or model architecture quickly within a single codebase.
Simplifies the experimental loop, allowing for rapid testing of changes to the inference pipeline.
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