Implementing Large Language Models Inference in Pure C++ - A Llama 2 Case Study

Overview

Dive into implementing Llama 2 model inference using pure C++, exploring dependency-free solutions and optimization techniques for efficient language model deployment.

Syllabus

• Introduction to Large Language Models
Overview of Language Models
Introduction to Llama 2
Key Features of Llama 2
• Environment Setup for C++ Development
Tools and Compilers for C++
Setting Up a Coding Environment
Introduction to Build Systems
• Fundamentals of C++
Key C++ Concepts
C++ Data Structures
Memory Management in C++
• Understanding Llama 2's Architecture
Model Architecture Overview
Input and Output Structure
Computational Graphs
• Implementing Model Inference in Pure C++
Key Components Required for Inference
Writing C++ Code for Model Layers
Handling Weights and Biases
• Optimization Techniques
Code Optimization Strategies
Memory Efficiency Improvements
Utilizing Parallel Processing
• Dependency-Free Solutions
Techniques for Eliminating Dependencies
Implementing Custom Matrix Operations
Serialization and Deserialization
• Testing and Validation
Unit Testing in C++
Validating Model Output
Performance Testing
• Deployment Strategies
Deploying C++ Applications
Examples of Real-World Deployments
Monitoring and Maintenance
• Conclusion and Future Directions
Recap of Key Learnings
Future Trends in Language Model Deployment
Continuing Education and Resources

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Subjects

Programming