The Llama 3.2 3B Instruct model features 3 billion parameters and is specifically designed for instruction-following tasks in a text-only format. This model utilizes an optimized transformer architecture that allows it to generate coherent and contextually relevant responses based on user instructions. It has been fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF), which enhances its ability to follow complex commands and provide helpful outputs. With a context length of up to 128,000 tokens, this model is capable of handling extensive input data, making it suitable for applications that require detailed interactions, such as chatbots or virtual assistants.