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Zero to Hero in Ollama: Create Local LLM Applications
Published 9/2024
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 3h 4m | Size: 1.13 GB[/center]

Run customized LLM models on your system privately | Use ChatGPT like interface | Build local applications using Python

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.

Requirements
Basic Python knowledge and a computer capable of running Docker and Ollama are recommended, but no prior AI experience is required.





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