Run customized LLM models on your system privately | Use ChatGPT like interface | Build local applications using Python
Sub Category
- Data Science
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Objectives
- 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.
Pre Requisites
- Basic Python knowledge and a computer capable of running Docker and Ollama are recommended, but no prior AI experience is required.
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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