How to Add Your Own Knowledge Files and Documents to a Large Language Model
Learn how to add your own knowledge files and documents to large language models (LLMs), both locally and online. We'll explore different methods like retraining, RAG, and context window uploading.
Course Timeline
🎥 Introduction
Course overview and a quick demo of a modestly sized model running locally.
🚀 Model Performance Comparison
Comparison of a 1 billion parameter model and a 70 billion parameter model's performance, highlighting speed differences in token generation.
📚 Methods for Adding Information to LLMs
Explains three primary methods for adding information: retraining, retrieval augmented generation (RAG), and uploading documents directly into the context window.
🤔 Why Not Retraining?
Discusses the reasons why retraining LLMs is impractical for most users, focusing on accessibility, hardware/software requirements, and coding expertise.
📌 Uploading Documents to ChatGPT's Context Window
A step-by-step guide on uploading documents to ChatGPT, demonstrating how to use uploaded documents for context-specific answers.
🤖 Creating a Custom GPT with Context Documents
Shows how to create a custom GPT instance within ChatGPT, uploading relevant documents to form its knowledge base.
💻 Uploading Documents to Local Llama Model
Demonstrates how to upload documents to a local Llama model using Open Web UI, enabling context-aware responses and referencing source documents.
🔎 Retrieval Augmented Generation (RAG)
Explains RAG as a dynamic information retrieval system, contrasting it with the static context window approach and highlighting its advantages for large or evolving data sets.
⚙️ Setting up RAG with Llama and Open Web UI
A step-by-step guide on configuring RAG within the Llama and Open Web UI system, involving document scanning, registration, and model creation.
💪 Using the RAG-Enabled Custom Model
Demonstrates using the newly created RAG-enabled custom model to answer specific questions, showcasing the retrieval and synthesis of information from the documents.
🎉 Conclusion
Summary of the discussed methods, emphasizing their strengths and use cases. Also includes a call to action.