How to Fine Tune Llama 3 for Better Instruction Following?

🚀 In today's video, I'm thrilled to guide you through the intricate process of fine-tuning the LLaMA 3 model for optimal instruction following! From setting up your environment to saving and uploading your customised model on Hugging Face, I'll cover everything you need to know. 📈 Whether you're a beginner or an experienced AI enthusiast, this tutorial is designed to enhance your skills and provide insights into the importance of model fine-tuning. 🤖💡 Massed compute: https://bit.ly/mervin-praison Coupon: MervinPraison (50% Discount) Connect to Massed Compute after Deploy: https://www.cendio.com/thinlinc/download/ 👉 What You'll Learn: Setup and Configuration: How to prepare your system for fine-tuning. Training the Model: Detailed steps on how to train your LLaMA 3 model using a specialized dataset. Saving and Uploading: Best practices for saving your model and uploading it to Hugging Face for community use. 📌 Timestamps: 0:00 Introduction to Fine-Tuning LLaMA 3 0:53 Step-by-Step Setup and Configuration 3:19 Training the Model with Real Data 5:58 Evaluating the Model Pre and Post Training 7:25 Saving and Uploading the Model to Hugging Face 👍 Why Fine-Tune? Fine-tuning allows you to adapt a base AI model to better understand and respond to specific instructions, drastically improving its accuracy and relevance in practical applications. 🔗 Resources: Patreon: https://patreon.com/MervinPraison Ko-fi: https://ko-fi.com/mervinpraison Discord: https://discord.gg/nNZu5gGT59 Twitter / X : https://twitter.com/mervinpraison Code: https://mer.vin/2024/04/llama-3-fine-tune-with-custom-data/ 💾 Resources & Tools Mentioned: Hugging Face 🤗 Conda Environments Python and IPython Packages Weights & Biases for Tracking Metrics Open Instruction Generalist Dataset 🔔 Subscribe and hit the bell to stay updated on our latest tutorials on Artificial Intelligence. Like this video to help others discover it, and share your thoughts and questions in the comments below! #LLaMA3