Fine-tuning ANY Open Source Model like a Pro

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AI Summary

Fine-Tuning Okar2 Model: A Beginner’s Guide

• Introduction
  • Excitement about demonstrating the fine-tuning of the Okar2 model.
  • Encouragement to subscribe and like the YouTube channel for AI content.
• Configuration Steps
  • Install necessary libraries with pip: pandas, Ludwig, matplotlib, PFT Auto, gptq, Optimum.
  • Create app.py and import required modules.
• Preparing Data
  • Create a QA pair list with questions and answers.
  • Emphasize the importance of data quantity for model accuracy.
• Defining Sequence Length
  • Write a function to determine the sequence length from the data.
  • Plot and save the graph of sequence lengths to identify the maximum length.
• Model Configuration
  • Set up Ludwig configuration with input and output features.
  • Specify model details: type, base model, quantization, and training parameters.
• Training the Model
  • Define the model with Ludwig and set logging level.
  • Train the model with the data frame and save the results in a folder.
• Prediction and Testing
  • Use a subset of data for testing predictions.
  • Print output for reference.
• Summary of Process
  • Load data into a DataFrame.
  • Calculate and use sequence length in the configuration.
  • Train the model and save the results.
• Running the Code
  • Execute the script in the terminal.
  • Observe the training process and final results.
• Uploading to Hugging Face
  • Push the trained model to Hugging Face.
  • Provide instructions for testing the model.
• Conclusion
  • Encouragement to like, share, and subscribe for future tutorials.