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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.

C
Added on 2025年5月6日
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llama2.c - Pure C Implementation of Llama 2 Inference preview
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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

C (C99)

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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