Mistral Finetuning on Custom Data: Learn in 7 Mins!

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AI Summary

Summary: Fine-Tuning a Language Model with Ludwig

• Introduction
  • Excitement about demonstrating Ludwig, a low-code framework for fine-tuning language models.
  • Ludwig’s features: ease of use, scalability, expert control, modularity, extensibility, and production readiness.
• Setup
  • Hardware specifications provided for reference.
  • Installation of Ludwig, Ludwig LLM, and PFT using pip.
  • Creation of app.py and setup of necessary imports (OS, yaml, logging, Ludwig model).
• Configuration
  • Configuration of Ludwig for the Mistral model with 7 billion parameters.
  • Setting up model type, quantization, Lura adapter, prompt template, input/output features, and training parameters.
  • Sample ratio for pre-processing set to 0.1.
• Training
  • Loading the configuration with yaml’s Safe Load.
  • Training the model using the Alpaka dataset for instruction following.
  • Saving the trained model.
• Execution
  • Running the code in the terminal with the Hugging Face API key.
  • Fixing yaml formatting issues for proper execution.
  • Observing the training process and dataset statistics.
• Results
  • The trained model can now respond to instructions rather than just completing text.
  • Example outputs demonstrate the model’s ability to answer questions.
  • Training and validation results, including loss metrics, are displayed.
  • The best model is saved to a specified folder.
• Uploading to Hugging Face
  • Instructions for uploading the trained model to the Hugging Face Hub.
  • Accessing the model on Hugging Face for use in applications.
• Conclusion
  • Encouragement to like, share, and subscribe for future tutorials.
  • Mention of further topics like underfitting and overfitting to be covered in upcoming videos.