Build your own LLM chatbot from scratch | End to End Gen AI | End to End LLM | Mistrak 7B LLM

How to Build Your Own LLM Chatbot from Scratch

Step 1: Understand the Concepts Behind AI Generative AI and Large Language Models

• Watch the video to get insights into the concepts behind AI, generative AI, and large language models like Chad GPT.
• Learn about generative AI, large language models, and their role in generating text, images, music, and videos.

Step 2: Explore Different Types of Large Language Models

• Understand the basics of large language models and their significance in artificial intelligence.
• Learn about different large language models available in the market, including Chad GPT, Google BERT, and others.

Step 3: Dive into Fine-Tuning a Model for Chatbot Creation

• Install necessary dependencies like Accelerate, PBits, Bytes, and Transformers for fine-tuning the model.
• Create an account on Hugging Face Hub and obtain an API key for model access.

Step 4: Process Data and Prepare the Model for Training

• Load a conversation dataset and convert it into a Pandas DataFrame for easier manipulation.
• Create a tokenizer, quantization config, and prepare the model for 4-bit training using AutoModel and AutoTokenizer.

Step 5: Train the Model for Chatbot Creation

• Set up training arguments including learning rate, gradient accumulation steps, and optimizer for fine-tuning the model.
• Train the model using the SF Trainer for supervised fine-tuning and evaluate the model's performance.

Step 6: Implement the Chatbot in a Flask Application

• Create a Flask application for deploying the chatbot for user interaction.
• Use HTML templates, Flask routes, and JavaScript to enable user input and display chatbot responses.

Step 7: Test the Chatbot Interface

• Run the Flask application to interact with the chatbot through the user interface.
• Input queries or messages to the chatbot and observe the responses generated by the fine-tuned model.

Step 8: Modify and Customize the Chatbot for Your Use Case

• Understand the structure of the chatbot code and make modifications as needed for your specific use case.
• Experiment with different configurations, generation settings, and input formats to tailor the chatbot to your requirements.

By following these steps, you can build your own LLM chatbot from scratch using the Mistrak 7B LLM model and deploy it in a Flask application for real-time interactions. Enjoy creating and customizing your chatbot for various applications and scenarios!