Critical Mistake Prevention Guide
You need to pay special attention to the following points when using MiniMind-V:
- Data preparation phase::
- Ensure that the images and jsonl files strictly match (review using the cleaning script provided by the project)
- Verify that the image size meets the 224 x 224 requirement (check using PIL.Image)
- Check for correct data paths, especially relative path issues
- training process::
- Monitor GPU utilization to make sure it is not idle (nvidia-smi -l 1 recommended)
- Note if the loss curve is falling normally (check learning rate if abnormal)
- Regularly save checkpoints to prevent unplanned outages
- inference stage::
- Strictly follow the @@@@...@@@n input format requirements
- Verify that the CLIP model was loaded successfully (check console output)
- Testing the effect of different temperature values on generation quality
The project has built-in several defensive programming checks: 1) Automatic skipping of corrupted images 2) Out of memory warning 3) Data type validation. New users are advised to test the officially provided pre-training weights before trying the full training process.
This answer comes from the articleMiniMind-V: 1 hour training of a 26M parameter visual language modelThe
May not be reproduced without permission:AI productivity tools " How to avoid common mistakes in visual language model training?
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