Unsloth - How to Train LLM 5x Faster and with Less Memory Usage?

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AI Summary

- Introduction to fine-tuning with UNS sloth  
  - Fine-tunes models like mral Jemma llama 2  
  - 5x faster, 70% less memory, no accuracy loss  
  - Supports Linux, Windows (WSL), 4bit/16bit quantization  
  - Outperforms Hugging Face in benchmarks  
  
- Fine-tuning demonstration  
  - Fine-tune a 7 billion parameter model (Mr 7)  
  - Example: Improving responses for business plan tips  
  - Using the OI IG dataset for instruction following  
  
- Tutorial steps  
  - Subscribe to the YouTube channel for AI content  
  - Load data and model  
  - Compare pre and post fine-tuning results  
  - Upload the model to Hugging Face  
  
- Setup instructions  
  - Create a conda environment with Python 3.11  
  - Install necessary packages (Hugging Face Hub, IPython, UNS sloth)  
  - Set up Hugging Face token for model upload  
  
- Code walkthrough  
  - Import necessary libraries and load the OI IG dataset  
  - Load and prepare the large language model (mro)  
  - Define functions for text generation  
  - Install additional package (onslot collab)  
  - Run the code to see pre-training response  
  
- Training process  
  - Model patching with fast lower weights  
  - Define supervised fine-tuning trainer (sft trainer)  
  - Train the model and observe the loss  
  - Save the trained model and adapters  
  
- Uploading to Hugging Face  
  - Save and push the merged model and adapter to Hugging Face  
  - Modify code to disable text streamer if needed  
  - Test the uploaded model with a sample question  
  
- Conclusion  
  - Fine-tuning is faster and more memory-efficient  
  - Encouragement to like, share, and subscribe for more videos