karpathy/llama2.c
Llama2.c
Llama2.c is a minimal inference engine designed to execute transformer-based language models using only standard C code. By implementing neural network forward passes without external dependencies or complex runtime environments, it provides a lightweight execution environment for running pre-trained models.
The project distinguishes itself through a focus on portability and resource efficiency. It utilizes static memory allocation to avoid dynamic heap management and maps model parameter files directly into the process address space to minimize memory overhead. The implementation relies on standard library functions and optimized linear algebra routines to perform matrix multiplication, ensuring the engine can operate across diverse hardware environments.
Beyond inference, the repository includes utilities for training custom tokenizers, allowing users to generate vocabulary files and define tokenization rules from raw text data. This combination of model execution and data preparation tools serves as a resource for studying the fundamental mechanics of transformer architectures and deploying neural networks in environments with limited processing power.
Features
- Inference Engines - Implements a minimal C-based engine for running transformer-based language models through forward passes.
- Large Language Models - Executes transformer-based language models in resource-constrained environments using standard C code.
- Minimalist Inference Runtimes - Runs pre-trained language models through forward passes in a minimal, dependency-free environment.
- Local Model Runners - Provides a lightweight execution environment for performing neural network inference on pre-trained language models.
- Single-Header Libraries - Implements neural network inference using a portable, single-header C codebase without external dependencies.
- Edge AI Deployment Pipelines - Enables neural network execution on resource-constrained hardware through a minimal and portable codebase.
- Large Language Models - Minimalist C-based inference engine for Llama 2 architecture.
- Memory-Mapped File Access - Maps model parameter files directly into process memory to minimize overhead and improve loading efficiency.
- Text Tokenizers - Includes tools for training custom tokenizers and defining rules for raw text processing.
- Static Memory Allocations - Uses static memory allocation to manage buffers and ensure predictable performance during inference.
- Text Tokenization Utilities - Provides utilities for creating vocabulary files and tokenization rules from raw text data.
- Dataset Tokenization Tools - Provides utilities for generating vocabulary files and defining tokenization rules from raw text data.
- Bytecode-Free Inference Engines - Executes neural network weights directly without requiring intermediate bytecode or complex runtime interpreters.
