Unlock the full potential of LLaMA 3.1 by learning how to optimize this powerful AI model using your own custom data! In this video, we walk you through training LLaMA 3.1 and storing it on Hugging Face and Olama, step by step. Perfect for companies that want to use AI with their private data!

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What you will 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. Model evaluation before training
3. Load data and train with SFT Trainer
4. Evaluation and storage of the model after training
5. Upload the model to Hugging Face & Olama ️

Advantages:
• Individual AI model tailored to your specific needs
• Easy deployment and accessibility on multiple platforms
• Improved performance with lower memory usage

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Code: https://mer.vin/2024/07/llama-3-1-fine-tune/

0:00 – Introduction to fine-tuning LLaMA 3.1
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 the results
10:16 – Saving the model in Ollama
10:36 – Create GGUF format
11:34 – Create Ollama model file
12:32 – Creating the model in Ollama
12:57 – Testing the model with Ollama
13:22 – Bringing the model to Ollama
14:17 – Final steps and conclusion

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