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

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... Working with Llama 3: https://www.datacamp.com/courses/work... Fine-Tuning with Llama 3: https://www.datacamp.com/courses/fine... Tutorial - Llama 3.3: Step-by-Step Tutorial With Demo Project: https://www.datacamp.com/tutorial/lla... Tutorial - Llama 3.2 and Gradio Tutorial: Build a Multimodal Web App: https://www.datacamp.com/tutorial/lla... Tutorial - Fine-tuning Llama 3.2 and Using It Locally: A Step-by-Step Guide: https://www.datacamp.com/tutorial/fin... 📱 Follow Us for More AI & Data Science Content Facebook:   / datacampinc   Twitter:   / datacamp   LinkedIn:   / datacampinc   Instagram:   / datacamp   #Llama3 #FineTuning #LoRA #HuggingFace #MachineLearning #AI #Quantization #DataScience #LLM #LangChain #MetaAI