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...Ollama also integrates with popular developer tools and AI agents, allowing seamless workflows across coding environments and applications. It supports REST APIs, Python, and JavaScript SDKs, making it easy to build AI-powered features into software projects. Overall, Ollama focuses on privacy, local-first AI execution, and developer-friendly tooling for building with open models.

The llama.cpp project enables the inference of Meta's LLaMA model (and other models) in pure C/C++ without requiring a Python runtime. It is designed for efficient and fast model execution, offering easy integration for applications needing LLM-based capabilities. The repository focuses on providing a highly optimized and portable implementation for running large language models directly within C/C++ environments.

llama2.c is a minimalist implementation of the Llama 2 language model architecture designed to run entirely in pure C. Created by Andrej Karpathy, this project offers an educational and lightweight framework for performing inference on small Llama 2 models without external dependencies. It provides a full training and inference pipeline: models can be trained in PyTorch and later executed using a concise 700-line C program (run.c). While it can technically load Meta’s official Llama 2...

...The project focuses on simplicity and educational clarity by implementing inference for LLaMA-style models in a compact C program rather than relying on large machine learning frameworks. Developers can train models using a Python training pipeline and then run inference using a lightweight C implementation that requires very few dependencies. The architecture mirrors the structure of the LLaMA-2 model family, allowing compatible model checkpoints to be converted and executed within the simplified runtime environment. Because the implementation is intentionally minimal, it serves as a teaching tool for understanding how transformer architectures operate at a low level.

Python bindings for the Transformer models implemented in C/C++ using GGML library.