Course Hive
Search

Welcome

Sign in or create your account

Continue with Google
or
LLM Fine Tuning Tutorial (Free Labs)
Play lesson

Learn AI with KodeKloud - LLM Fine Tuning Tutorial (Free Labs)

5.0 (2)
19 learners

What you'll learn

This course includes

  • 17.5 hours of video
  • Certificate of completion
  • Access on mobile and TV

Summary

Keywords

Full Transcript

🧪 Fine-Tune LLMs & Build Real AI Agents — https://kode.wiki/4cHnB48 Prompt engineering is fragile. Users can override your system prompt, break character, and inject instructions you never intended. Fine-tuning actually changes the model's weights — embedding behavior directly into how it thinks. This video walks you through why fine-tuning beats prompt engineering for production AI agents, how LoRA and QLoRA make it feasible on consumer hardware, and how to build a Taco Drive-Through agent that stays on topic and resists jailbreaks — inside a real KodeKloud hands-on lab. No theory overload. Just structured, practical learning from the problem all the way to alignment testing. ───────────────────────────────────────── 📌 WHAT YOU'LL LEARN IN THIS VIDEO ───────────────────────────────────────── ✅ How prompt engineering gets hacked and why fine-tuning is the fix ✅ How RLHF turned GPT-3 into ChatGPT ✅ Real use cases: guaranteed JSON output, brand agents, and game NPCs ✅ How LoRA and QLoRA freeze base parameters and add lightweight adapter layers ✅ All 6 Fine-Tuning steps: prompt problem → data prep → LoRA config → training → evaluation → alignment 🧪 FREE HANDS-ON LAB — https://kode.wiki/4cHnB48 Practice everything in a real sandbox. No local setup, no credit card, no surprises. GPU environment, dependencies, and all lab tasks are pre-configured and ready to go. ⏱️ TIMESTAMPS 00:00 – Introduction to Fine-Tuning LLMs 00:45 – Prompt Engineering: What It Is and Why It Falls Short 01:40 – Fine-Tuning Explained 02:03 – Real Use Cases 02:45 – LoRA and QLoRA 03:12 – Lab Intro: Taco Drive-Through Agent 04:16 – Lab - Setting up the environment 04:35 – Task 1: The Prompt Engineering Problem 05:40 – Task 2: Preparing Training Data 06:20 – Task 3: Configuring LoRA 07:35 – Task 4: Training with LoRA 08:22 – Task 5: Test Fine-Tuned Agent 09:03 – Task 6: Create DPO Preference Data 10:11 – Key Takeaways #LLMFineTuning #LoRA #QLoRA #AIAgent #PromptEngineering #RLHF #KodeKloud #MachineLearning #GenerativeAI #DeepLearning #AITutorial #MLOps #OpenAI #FineTuneGPT #AIEngineering #HandsOnLab #LargeLanguageModels #AITraining #LearnAI #DevOpsAI #NLP #LLMTraining #ParameterEfficientFineTuning #CloudAI #AIJailbreak

Course Hive

Continue this lesson in the app

Install CourseHive on Android or iOS to keep learning while you move.

Related Courses

FAQs

Course Hive
Download CourseHive
Keep learning anywhere