Announcement
llama2.c - Pure C Implementation of Llama 2 Inference
A minimalist, single-file implementation of Llama 2 inference in pure C, designed for simplicity and educational purposes.
Project Introduction
Summary
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.
Problem Solved
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.
Core Features
Single-File Implementation
Entire inference code contained within a single .c file for maximum simplicity and portability.
Pure C (C99)
Written exclusively in pure C (C99 standard), with no external dependencies beyond standard libraries.
Educational Focus
Focuses on clarity and readability to serve as an educational tool for understanding LLM inference.
Minimalist Design
Includes minimal necessary components for loading weights and running inference.
Tech Stack
使用场景
The simplicity and pure C nature of this project make it suitable for various learning, experimentation, and integration scenarios:
学习与研究
Details
Use the code as a reference to understand the forward pass computation, token sampling, and weight loading process of Llama 2.
User Value
Provides a clear, executable example for educational purposes, supplementing theoretical knowledge.
嵌入式与系统集成
Details
Integrate the core inference logic into C/C++ projects or embed it on devices with limited resources where complex ML frameworks are not feasible.
User Value
Enables deploying LLM capabilities in new environments due to its minimal footprint and lack of external dependencies.
原型设计与快速迭代
Details
Modify and experiment with the inference process or model architecture quickly within a single codebase.
User Value
Simplifies the experimental loop, allowing for rapid testing of changes to the inference pipeline.
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