The video introduces AutoCoder, a large language model that surpasses GPT-4 Turbo and GPT-4o in the Human Eval benchmark test. AutoCoder creates a coding process that closely resembles human code generation. Its responses are coherent, respectful, and empathetic. The model also offers a more versatile code interpreter that can install external packages, unlike its competitors. AutoCoder’s training data is a multi-turn dialogue dataset created by a system that combines agent interaction and external code execution verification. This method, known as AE Instruct, reduces dependency on proprietary large models and provides execution validated code data. AutoCoder comes in two versions: 6.7 billion and 33 billion. The 33 billion version has beaten GPT-4 Turbo and GPT-4o in the Human Eval Benchmark. The video also guides viewers on how to install AutoCoder locally.