Zero to Hero in Ollama: Create Local LLM Applications

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  • At udemy.com you can purchase Zero to Hero in Ollama: Create Local LLM Applications for only $49.99
  • The lowest price of Zero to Hero in Ollama: Create Local LLM Applications was obtained on November 22, 2024 5:54 am.

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Last updated on November 22, 2024 5:54 am
Zero to Hero in Ollama: Create Local LLM Applications
Zero to Hero in Ollama: Create Local LLM Applications

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Zero to Hero in Ollama: Create Local LLM Applications

★★★★★
$49.99  in stock
Udemy.com
as of November 22, 2024 5:54 am

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

Created by: Start-Tech Academy
5,000,000+ Enrollments | 4.5 Rated | 160+ Countries
Rating:4.65 (73reviews)     4652students enrolled

What Will I 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.

Target audience

  • 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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