Unlock the full potential of LLaMA 3.1 by learning how to fine-tune this powerful AI model using your own custom data! 🚀 In this video, we’ll take you through a step-by-step guide to train LLaMA 3.1, save it on Hugging Face, and Olama. Perfect for businesses looking to leverage AI with their private data! 🌟
Massed compute: https://bit.ly/mervin-praison
Coupon: MervinPraison (50% Discount)
🔍 What You’ll Learn:
• Why fine-tuning is essential for custom data 📊
• Training the 8 billion parameter LLaMA 3.1 model 🦙
• How to save and deploy your model on Hugging Face and Olama 🌐
🔧 Steps Covered:
1. Configuration setup and data formatting ⚙️
2. Pre-training model evaluation 📉
3. Data loading and training with SFT Trainer 📥
4. Post-training model evaluation and saving 🚀
5. Uploading the model to Hugging Face & Olama 🛠️
💡 Benefits:
• Custom AI model tailored to your specific needs 🎯
• Easy deployment and accessibility on various platforms 🌍
• Enhanced performance with less memory usage 💾
🔗 Links:
Patreon: https://patreon.com/MervinPraison
Ko-fi: https://ko-fi.com/mervinpraison
Discord: https://discord.gg/nNZu5gGT59
Twitter / X : https://twitter.com/mervinpraison
GPU for 50% of it's cost: https://bit.ly/mervin-praison Coupon: MervinPraison (50% Discount)
Code: https://mer.vin/2024/07/llama-3-1-fine-tune/
0:00 - Introduction to LLaMA 3.1 fine-tuning
1:07 - Overview of the video content
2:29 - Configuration
4:52 - Loading the dataset
6:40 - Training the model
8:12 - Saving the model
9:13 - Running the code and observing results
10:16 - Saving the model to Ollama
10:36 - Creating GGUF format
11:34 - Creating Ollama Modelfile
12:32 - Creating the model in Ollama
12:57 - Testing the model with Ollama
13:22 - Pushing the model to Ollama
14:17 - Final steps and conclusion