Anyone can Fine Tune LLMs using LLaMA Factory: End-to-End Tutorial

Tutorial: How to Fine-Tune Large Language Models Using LLaMA Factory

1. Introduction to LLaMA Factory:

   • LLaMA Factory is a recent framework designed to fine-tune large language models (LLMs) with ease.
   • It supports over 100 datasets and 50 LLMs, utilizing techniques like PoD, DPO, SFT, reward modeling, and more.

2. Setting Up LLaMA Factory:

   • Clone the LLaMA Factory repository by running the command: git clone [repository_url].
   • Navigate to the cloned directory and install dependencies using pip install -r requirements.txt.

3. Configuring Data Set and Templates:

   • Modify the dataset_info.json file to define your dataset, including instructions, queries, and responses.
   • Customize the prompt templates in the template.py file to match your specific use case.

4. Training and Fine-Tuning:

   • Run the training script using CUDA with CUDA_VISIBLE_DEVICES=0 python src/train_web.py.
   • Monitor the training process, including loss graphs and model adaptation.

5. Evaluating and Predicting:

   • After training, refresh adapters and load the fine-tuned model.
   • Use the chat interface to interact with the model, input queries, and receive responses.

6. Exporting and Personalizing the Model:

   • Explore export options to customize model settings like quantization bits and maximum size.
   • Evaluate the model's performance and predict responses using the provided tools.

7. Final Steps and Recommendations:

   • Ensure to save and back up your fine-tuned model for future use.
   • Experiment with different inference parameters to optimize model performance.

8. Conclusion:

   • LLaMA Factory provides a user-friendly interface for fine-tuning LLMs, making it accessible to both technical and non-technical users.
   • Continuously learn and understand the underlying algorithms and fundamentals to enhance your skills in artificial intelligence and machine learning.

9. Feedback and Sharing:

   • Share your experience with using LLaMA Factory and provide feedback to the community.
   • Stay updated on AI Anytime's content and engage with the community through social media channels.

By following these steps, you can effectively utilize LLaMA Factory to fine-tune large language models for various applications and domains.