Open-source LLMs: Uncensored & secure AI locally with RAG

Private ChatGPT Alternatives: Llama3, Mistral a. more with Function Calling, RAG, Vector Databases, LangChain, AI-Agents

4.71 (1351 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Why Open-Source LLMs? Differences, Advantages, and Disadvantages of Open-Source and Closed-Source LLMs

What are LLMs like ChatGPT, Llama, Mistral, Phi3, Qwen2-72B-Instruct, Grok, Gemma, etc.

Which LLMs are available and what should I use? Finding "The Best LLMs"

Requirements for Using Open-Source LLMs Locally

Installation and Usage of LM Studio, Anything LLM, Ollama, and Alternative Methods for Operating LLMs

Censored vs. Uncensored LLMs

Finetuning an Open-Source Model with Huggingface or Google Colab

Vision (Image Recognition) with Open-Source LLMs: Llama3, Llava & Phi3 Vision

Hardware Details: GPU Offload, CPU, RAM, and VRAM

All About HuggingChat: An Interface for Using Open-Source LLMs

System Prompts in Prompt Engineering + Function Calling

Prompt Engineering Basics: Semantic Association, Structured & Role Prompts

Groq: Using Open-Source LLMs with a Fast LPU Chip Instead of a GPU

Vector Databases, Embedding Models & Retrieval-Augmented Generation (RAG)

Creating a Local RAG Chatbot with Anything LLM & LM Studio

Linking Ollama & Llama 3, and Using Function Calling with Llama 3 & Anything LLM

Function Calling for Summarizing Data, Storing, and Creating Charts with Python

Using Other Features of Anything LLM and External APIs

Tips for Better RAG Apps with Firecrawl for Website Data, More Efficient RAG with LlamaIndex & LlamaParse for PDFs and CSVs

Definition and Available Tools for AI Agents, Installation and Usage of Flowise Locally with Node (Easier Than Langchain and LangGraph)

Creating an AI Agent that Generates Python Code and Documentation, and Using AI Agents with Function Calling, Internet Access, and Three Experts

Hosting and Usage: Which AI Agent Should You Build and External Hosting, Text-to-Speech (TTS) with Google Colab

Finetuning Open-Source LLMs with Google Colab (Alpaca + Llama-3 8b, Unsloth)

Renting GPUs with Runpod or Massed Compute

Security Aspects: Jailbreaks and Security Risks from Attacks on LLMs with Jailbreaks, Prompt Injections, and Data Poisoning

Data Privacy and Security of Your Data, as well as Policies for Commercial Use and Selling Generated Content

Course Gallery

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Open-source LLMs: Uncensored & secure AI locally with RAG – Screenshot 2 — Screenshot 2 – Open-source LLMs: Uncensored & secure AI locally with RAG

Open-source LLMs: Uncensored & secure AI locally with RAG – Screenshot 3 — Screenshot 3 – Open-source LLMs: Uncensored & secure AI locally with RAG

Open-source LLMs: Uncensored & secure AI locally with RAG – Screenshot 4 — Screenshot 4 – Open-source LLMs: Uncensored & secure AI locally with RAG

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Comidoc Review

Our Verdict

Open-source LLMs: Uncensored & secure AI locally with RAG offers an engaging and practical approach to understanding and implementing private chatGPT alternatives. While it does not provide comprehensive coverage of coding or theoretical concepts, the course excels at offering valuable insights into using tools like Llama3, Mistral, and more for various applications. However, if you're looking for a deeper dive into LLM differentiation or are focused on hands-on coding experiences, this course may not be your best fit.

What We Liked

In-depth exploration of open-source Large Language Models (LLMs) like Llama3, Mistral, and more, providing an attractive alternative to censored and closed-source models.
Hands-on learning with practical examples, prompt engineering techniques, and cloud deployment insights, enabling you to create your own assistants in HuggingChat and utilize open-source LLMs with fast LPU chips.
Comprehensive introduction to various applications such as function calling, RAG, vector databases, LangChain, and AI-Agents for a wide range of scenarios, from data analysis to chatbot development.
Additional tools, tips, and resources provided, including text-to-speech with Google Colab, finetuning open-source LLMs with Google Colab, and renting GPUs from providers like Runpod or Massed Compute for insufficient local PCs.

Potential Drawbacks

Lacks in-depth coverage of coding or practical development, potentially limiting the learning experience for those seeking hands-on lessons.
Some course materials, such as the instructor's notebooks, are not directly linked, which may hinder following along closely with the course content for some learners.
Limited guidance on explaining differences or purposes of various LLM models, making it challenging for beginners to navigate the diverse landscape of LLMs.
Occasionally references uncovered topics like LangChain and LlamaIndex without detailed exploration, which may lead to confusion for those interested in these specific areas.

6047215

udemy ID

28/06/2024

course created date

15/07/2024

course indexed date

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