This project involved the creation of a chatbot utilizing open-source Large Language Models (LLMs) with a focus on delivering human-like conversational capabilities. Leveraging the powerful Hugging Face Transformers library, I implemented Facebook's BlenderBot model to understand and generate responses that mimic human conversation. The core functionality centered around tokenization, input processing, and response generation, ensuring the chatbot could handle ongoing dialogues with context awareness. The implementation was crafted to handle the intricacies of natural language understanding and generation, making the chatbot robust and responsive. It certainly has it's limitations, but it demonstrates the basic process of integrating AI models into user-friendly applications.

• Install Required Libraries: Set up the Python environment and installed necessary packages including Hugging Face’s Transformers.
• Model Selection: Chose Facebook’s BlenderBot for its efficiency and open-source accessibility.
• Model Initialization: Downloaded and initialized BlenderBot along with its tokenizer for processing user inputs.
• Conversation Management: Implemented a system to manage and store conversation history to maintain context in ongoing interactions.
• Input Processing: Developed a method to tokenize user input and chat history to prepare data for model processing.
• Response Generation: Utilized the model to generate responses based on the current conversation context.
• Output Handling: Decoded model outputs to convert them back into human-readable text.
• History Update: Updated the conversation history after each interaction to include the latest exchange.