Name: Zero to Hero in Ollama: Create Local LLM Applications
Availability: InStock

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.

AI enthusiasts who want to build and run customized LLM models privately on their local systems.
Python developers seeking to integrate large language models into local applications for enhanced functionality.
Data scientists who aim to create secure, private LLM-powered tools without relying on cloud-based solutions.
Machine learning engineers looking to explore and customize open-source models using Ollama and LangChain.
Tech professionals who want to develop RAG (Retrieval-Augmented Generation) applications using local data.
Privacy-conscious developers interested in running AI models with full control over data and environment.

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