How to Train a Multi Modal Large Language Model with Images?

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AI Summary

Summary: Fine-Tuning a Multimodal Model

• Objective: Enhance a multimodal model to provide detailed image descriptions.
• Model: EIX 9 billion parameter model fine-tuned with doodles.
• Desired Outcome: Model to describe images with added details.

Steps for Fine-Tuning:

1. Setup Configuration: Prepare the environment and dependencies.
2. Initial Model Check: Print model layers before fine-tuning.
3. Image Preparation: Convert images to RGB and resize.
4. Data Preparation: Tokenize images and split dataset into training and test sets.
5. Fine-Tuning: Adjust model with specific training arguments and start training.
6. Post-Training Check: Evaluate model’s performance after training.
7. Saving and Uploading: Save the fine-tuned model and upload to Hugging Face.

Tools and Commands:

• Environment Setup: Use conda and pip to install necessary libraries.
• Hugging Face Integration: Set environment variables for Hugging Face token and enable faster uploads.
• Code Execution: Run Python scripts to load, fine-tune, and test the model.

Additional Information:

• YouTube Channel: Creator provides AI-related content and tutorials.
• Discount Offer: Mention of a GPU rental service with a discount code.
• Note: Suggestion to use a Python notebook for step-by-step execution.

Final Outcome:

• The model successfully describes an image with detailed attributes post-training.
• The fine-tuned model is uploaded to Hugging Face for access.

Call to Action:

• Encouragement to like, share, subscribe, and stay tuned for similar content.