Fine-Tuning Poly JMA: A Google Efficient Language Model

Introduction

• Poly JMA: A new large language model by Google
• Part of the Gemma suite of open-source large language models
• Excels in image captioning, visual question answering, detection and expression segmentation, document understanding, and more
• Tutorial covers: downloading dataset, data preparation, training arguments, and model training
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Step-by-Step Tutorial

1. Downloading and Preparing the Dataset

• Modules to install/download: datasets, transformers, accelerate
• Recommendation: Log in to Hugging Face Hub (to save the model later)
• Dataset: Small subset of training data (approximately 2,000 samples)
  • Columns: multiple choice answer, question, image
  • Data inspection: various labels, questions, paths to images
• Data Preparation: Select relevant columns and split into training and testing subsets

2. Processing the Dataset

• Model Type: Vision model; requires specific preprocessing steps
• Processor: Download PolyJMA processor from Hugging Face Hub
• Data Conversion: Transform each input row into specialized tokens using a colate function
  • Implemented using PyTorch, CUDA for GPU processing
  • Define colate function to handle text, labels, images, tokens
  • Load data onto GPU for tokenization

3. Loading and Configuring the Model

• Import necessary modules: torch, etc.
• Model Download: Load PolyJMA model to the GPU from Hugging Face Hub
• Configuration: Set up LORA (Low-Rank Adaptation) configurations
  • Specify modules to be fine-tuned
  • Set BNB (Bits and Bytes) configuration
• Training Arguments: Specify epochs, batch size, learning rate, Adam beta, weight decay, and other parameters

4. Training the Model

• Define Trainer: Pass in the model, training data, colate function, and arguments
• Start Training: Note on duration; approx. 10 minutes on Google Colab
• Metrics and Results: Generated post-training
• Model Saving: Push model to Hugging Face Hub for storage
  • Provides a convenient link for inference and further usage of the saved model

Conclusion

• Quick and efficient method for fine-tuning Poly JMA
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• Call to action: see you in the next video!

Additional Notes

• Tools Used: Google Colab, Hugging Face Hub
• Potential Applications: Vision Transformers, various visual and document processing tasks
• Considerations: Model fine-tuning parameters may need to be adjusted for different use cases
• Community and Support: Hugging Face Hub offers a platform for model sharing and collaboration