Llama 3 Fundamentals Full Course | Master LLMs, Fine-Tuning, Hugging Face and LoRA

Unlock the power of Llama 3, Meta’s open-source large language model (LLM), and learn how to run, fine-tune, and optimize it for real-world applications. Whether you’re a beginner or an experienced AI practitioner, this full-course tutorial covers local deployment, fine-tuning techniques, LoRA, quantization, and Hugging Face integration to help you maximize efficiency.

📌 What You’ll Learn:
Running Llama 3 Locally: Set up and use llama-cpp-python to run Llama on your own machine for privacy, security, and cost efficiency.
Tuning Responses & Chat Roles: Adjust decoding parameters (temperature, top-k, top-p) and assign system/user roles for custom outputs.
Fine-Tuning with TorchTune & Hugging Face: Train Llama 3 on custom datasets using TorchTune, SFTTrainer, and LoRA for efficient model adaptation.
LoRA for Efficient Fine-Tuning: Use Low-Rank Adaptation (LoRA) to fine-tune models with minimal memory impact.
Quantization for Speed & Storage: Reduce model size with bitsandbytes for faster inference on lightweight hardware.
Multi-Turn Conversations: Build memory-aware assistants that track context for dynamic, real-time interactions.
Generating Structured Output: Extract JSON-formatted data from Llama 3 for automation and data processing.

📕 Video Highlights
00:00 Introduction to Llama 3
00:23 Course Overview and Expert Guidance
00:53 What is Llama 3?
01:33 Benefits of Running Llama Locally
02:06 Installing and Using Llama CPP Python
02:42 Querying Llama 3 for Text Generation
03:15 Understanding Response Structure
03:54 Tuning Llama's Responses
04:28 Adjusting Decoding Parameters
05:04 Temperature, Top-K, and Top-P Explained
06:08 Controlling Response Length with Max Tokens
07:15 Using Chat Rules to Customize Responses
07:45 Implementing System and User Roles
08:54 Structured Conversations with Create Chat Completion
10:17 Refining Prompts for Better Responses
10:50 Zero-Shot and Few-Shot Prompting
12:38 Using Stopwords to Control Output
13:09 Structuring JSON Responses
14:10 Defining JSON Output Formats
15:15 Using JSON Schemas for Consistency
16:22 Implementing Conversation Memory
17:30 Using Conversation History for Context
18:42 Summary of Llama 3 Fundamentals
19:54 Next Steps in Llama Learning
20:17 Introduction to Fine-Tuning Llama 3
21:03 Key Components of Fine-Tuning
22:17 Overview of Fine-Tuning Libraries
24:31 Preprocessing Data for Fine-Tuning
25:16 Using Hugging Face Datasets
27:27 Formatting Data for Training
28:10 Running a Fine-Tuning Job with TorchTune
29:45 Customizing Training Recipes
30:59 Running and Monitoring Training
32:40 Evaluating Model Performance
33:11 Using Rouge Score for Evaluation
35:23 Efficient Fine-Tuning with LoRA
37:02 Understanding Model Quantization
40:39 Fine-Tuning Large Models with Quantization
44:19 Final Thoughts and Course Conclusion

🖇️ Resources & Documentation

Check out our newly released newsletter on Substack — The Median: https://dcthemedian.substack.com
Take this skill track on DataCamp - Llama Fundamentals: https://www.datacamp.com/tracks/llama-fundamentals
Working with Llama 3: https://www.datacamp.com/courses/working-with-llama-3
Fine-Tuning with Llama 3: https://www.datacamp.com/courses/fine-tuning-with-llama-3
Tutorial - Llama 3.3: Step-by-Step Tutorial With Demo Project: https://www.datacamp.com/tutorial/llama-3-3-tutorial
Tutorial - Llama 3.2 and Gradio Tutorial: Build a Multimodal Web App: https://www.datacamp.com/tutorial/llama-gradio-app
Tutorial - Fine-tuning Llama 3.2 and Using It Locally: A Step-by-Step Guide: https://www.datacamp.com/tutorial/fine-tuning-llama-3-2

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Видео Llama 3 Fundamentals Full Course | Master LLMs, Fine-Tuning, Hugging Face and LoRA канала DataCamp
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