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Home > Data > GPT/LLMs > Harnessing Ollama - Create Secure Local LLM Solutions with Python
Harnessing Ollama - Create Secure Local LLM Solutions with Python
Harnessing Ollama - Create Secure Local LLM Solutions with Python

Harnessing Ollama - Create Secure Local LLM Solutions with Python: Master and Run Custom Ollama LLMs Privately on Your System with a ChatGPT-Like Interface

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Profile Icon Paulo Dichone
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€8.99 €82.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1 Ratings)
Video Dec 2024 3hrs 14mins 1st Edition
Video
€8.99 €82.99
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Arrow left icon
Profile Icon Paulo Dichone
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€8.99 €82.99
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1 Ratings)
Video Dec 2024 3hrs 14mins 1st Edition
Video
€8.99 €82.99
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Renews at €18.99p/m
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Table of content icon View table of contents Download code icon Download Code

Key benefits

  • Comprehensive hands-on projects for private LLM customization and deployment
  • Step-by-step guidance to build interactive applications without cloud reliance
  • Advanced workflows, including voice response and function calling integrations

Description

Discover how to deploy and harness the power of local LLMs with Ollama in this hands-on course. You'll begin by setting up and configuring Ollama on your system, gaining full control of large language models without relying on the cloud. Learn how to optimize resources, explore model parameters, and test various LLMs, including multimodal models like Llava, for text, vision, and code-generation tasks. Delve deeper into customizing models with the Modelfile and command-line tools to meet specific needs. The course covers executing terminal commands for monitoring, troubleshooting, and deploying models. You'll also integrate Ollama models with Python, leveraging its library and OpenAI API compatibility to build interactive applications. Advanced modules guide you through creating Retrieval-Augmented Generation (RAG) systems using LangChain, embedding databases, and querying capabilities for enhanced performance. As you progress, you'll set up ChatGPT-like interfaces for seamless model interaction and explore advanced workflows like function calling and voice-enabled RAG systems. By the end, you’ll master Ollama’s ecosystem, equipping you with the skills to build secure, private, and highly efficient LLM-based applications that can operate independently of cloud services.

Who is this book for?

This course is ideal for developers, AI enthusiasts, and data scientists eager to harness local LLMs for secure and private applications. Whether you're new to Ollama or experienced with AI systems, you'll benefit from its practical focus on deploying and customizing models. A basic understanding of Python and command-line tools is recommended, while prior experience with AI concepts, though helpful, is not required.

What you will learn

  • Design and deploy secure local LLM solutions with Ollama
  • Customize models to meet specific needs and applications
  • Execute commands to control and monitor LLMs effectively
  • Develop Python apps to integrate with Ollama models
  • Create RAG systems using LangChain and vector databases
  • Configure user-friendly interfaces for seamless interaction

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 23, 2024
Length: 3hrs 14mins
Edition : 1st
Language : English
ISBN-13 : 9781837020058
Category :
Data
Languages :
Python
Concepts :
GPT/LLMs

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Product Details

Publication date : Dec 23, 2024
Length: 3hrs 14mins
Edition : 1st
Language : English
ISBN-13 : 9781837020058
Category :
Data
Languages :
Python
Concepts :
GPT/LLMs

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Table of Contents

10 Chapters
Introduction Chevron down icon Chevron up icon
Introduction & What Will You Learn
Course Prerequisites
Please WATCH this DEMO
Development Environment Setup Chevron down icon Chevron up icon
Development Environment Setup
Ollama Deep Dive - Introduction to Ollama and Setup Chevron down icon Chevron up icon
Ollama Deep Dive - Ollama Overview - What is Ollama and Advantages
Ollama Key Features and Use Cases
System Requirements & Ollama Setup - Overview
Download and Setup Ollama and Llam3.2 Model - Hands-on & Testing
Ollama Models Page - Full Overview
Ollama Model Parameters Deep Dive
Understanding Parameters and Disk Size and Computational Resources Needed
Ollama CLI Commands and the REST API - Hands-on Chevron down icon Chevron up icon
Ollama Commands - Pull and Testing a Model
Pull in the Llava Multimodal Model and Caption an Image
Summarization and Sentiment Analysis & Customizing Our Model with the Modelfile
Ollama REST API - Generate and Chat Endpoints
Ollama REST API - Request JSON Mode
Ollama Models Support Different Tasks - Summary
Ollama - User Interfaces for Ollama Models Chevron down icon Chevron up icon
Different Ways to Interact with Ollama Models - Overview
Ollama Model Running Under Msty App - Frontend Tool - RAG System Chat with Docs
Ollama Python Library - Using Python to Interact with Ollama Models Chevron down icon Chevron up icon
The Ollama Python Library for Building LLM Local Applications - Overview
Interact with Llama3 in Python Using Ollama REST API - Hands-on
Ollama Python Library - Chatting with a Model
Chat Example with Streaming
Using Ollama show Function
Create a Custom Model in Code
Building LLM Applications with Ollama Models Chevron down icon Chevron up icon
Hands-on: Build a LLM App - Grocery List Categorizer
Building RAG Systems with Ollama - RAG & LangChain Overview
Deep Dive into Vectorstore and Embeddings - The Whole Picture - Crash Course
PDF RAG System Overview - What We'll Build
Setup RAG System - Document Ingestion & Vector Database Creation and Embeddings
RAG System - Retrieval and Querying
RAG System - Cleaner Code
RAG System - Streamlit UI
Ollama Tool Function Calling - Hands-on Chevron down icon Chevron up icon
Function Calling (Tools) Overview
Setup Tool Function Calling Application
Categorize Items Using the Model and Setup the Tools List
Tools Calling LLM Application - Final Product
Final RAG System with Ollama and Voice Response Chevron down icon Chevron up icon
Voice RAG System - Overview
Setup EleveLabs API Key and Load and Summarize the Document
Ollama Voice RAG System - Working!
Adding ElevenLab Voice Generated Reading the Response Back to Us
Wrap Up Chevron down icon Chevron up icon
Wrap Up - What's Next?

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Josh Dec 30, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A solid overview and a good pace. I learned a lot from taking this course that would have taken me many more hours on my own. The instructor's voice and cadence are great and the theoretical explanations provide an appropriate amount of context before diving into the code.
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About the author

Profile icon Paulo Dichone
Paulo Dichone
Paulo Dichone, a dedicated developer and educator in Android, Java, and Flutter, has empowered over 80,000 students globally with both soft and technical skills through his platform, Build Apps with Paulo. Holding a Computer Science degree and with extensive experience in mobile and web development, Paulo's passion lies in guiding learners to become proficient developers. Beyond his 5 years of online teaching, he cherishes family time, music, and travel, aiming to make impactful developers irrespective of their background.
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