Fine-Tuning Llama 3 on a Custom Dataset: Training LLM for a RAG Q&A Use Case on a Single GPU

Are you happy with your Large Language Model (LLM) performance on a specific task? If not, fine-tuning might be the answer. Even a simpler, smaller model can outperform a larger one if it's fine-tuned correctly for a specific task. In this video, you'll learn how to fine-tune Llama 3 on a custom dataset.

Model on HF: https://huggingface.co/curiousily/Lla...
Philipp Schmid Post: https://www.philschmid.de/fine-tune-l...

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GitHub repository: https://github.com/curiousily/AI-Boot...

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00:00 - Why fine-tuning?
00:25 - Text tutorial on MLExpert.io
00:53 - Fine-tuning process overview
02:19 - Dataset
02:56 - Lllama 3 8B Instruct
03:53 - Google Colab Setup
05:30 - Loading model and tokenizer
08:18 - Create custom dataset
14:30 - Establish baseline
17:37 - Training on completions
19:04 - LoRA setup
22:25 - Training
26:42 - Load model and push to HuggingFace hub
28:43 - Evaluation (comparing vs the base model)
32:50 - Conclusion

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