What you'll learn:
- Install and configure Ollama on your local system to run large language models privately.
- Customize LLM models to suit specific needs using Ollama’s options and command-line tools.
- Execute all terminal commands necessary to control, monitor, and troubleshoot Ollama models
- Set up and manage a ChatGPT-like interface using Open WebUI, allowing you to interact with models locally
- Deploy Docker and Open WebUI for running, customizing, and sharing LLM models in a private environment.
- Utilize different model types, including text, vision, and code-generating models, for various applications.
- Create custom LLM models from a gguf file and integrate them into your applications.
- Build Python applications that interface with Ollama models using its native library and OpenAI API compatibility.
- Develop a RAG (Retrieval-Augmented Generation) application by integrating Ollama models with LangChain.
- Implement tools and agents to enhance model interactions in both Open WebUI and LangChain environments for advanced workflows.
Are you looking to build and run customized large language models (LLMs) right on your own system, without depending on cloud solutions? Do you want to maintain privacy while leveraging powerful models similar to ChatGPT? If you're a developer, data scientist, or an AI enthusiast wanting to create local LLM applications, this course is for you!
This hands-on course will take you from beginner to expert in using Ollama, a platform designed for running local LLM models. You’ll learn how to set up and customize models, create a ChatGPT-like interface, and build private applications using Python—all from the comfort of your own system.
In this course, you will:
Install and customize Ollama for local LLM model execution
Master all command-line tools to effectively control Ollama
Run a ChatGPT-like interface on your system using Open WebUI
Integrate various models (text, vision, code generation) and even create your own custom models
Build Python applications using Ollama and its library, with OpenAI API compatibility
Leverage LangChain to enhance your LLM capabilities, including Retrieval-Augmented Generation (RAG)
Deploy tools and agents to interact with Ollama models in both terminal and LangChain environments
Why is this course important? In a world where privacy is becoming a greater concern, running LLMs locally ensures your data stays on your machine. This not only improves data security but also allows you to customize models for specialized tasks without external dependencies.
You’ll complete activities like building custom models, setting up Docker for web interfaces, and developing RAG applications that retrieve and respond to user queries based on your data. Each section is packed with real-world applications to give you the experience and confidence to build your own local LLM solutions.
Why this course? I specialize in making advanced AI topics practical and accessible, with hands-on projects that ensure you’re not just learning but actually building real solutions. Whether you’re new to LLMs or looking to deepen your skills, this course will equip you with everything you need.
Ready to build your own LLM-powered applications privately? Enroll now and take full control of your AI journey with Ollama!
