Update README.md
Browse files\- Minimal instructions to run a quick local debug, full training, save outputs to `./out_dir`, export processor/config, save evaluation results JSON, create a `MODEL_CARD.md`, and push to the Hugging Face Hub.
Requirements
\- Python 3.9+ on Windows
\- Add to `requirements.txt`😀
1) Quick local debug run (tiny subset)
\- Purpose: verify training loop and I/O without long runtime.
\- Edit `main.py` or run a small wrapper to limit data and epochs. Example pattern to use inside `main.py` before creating dataloaders:
```python
# debug config (temporary)
import os
DEBUG = os.getenv('DEBUG', '0') == '1'
if DEBUG:
num_epochs = 20
# use a small subset of CIFAR-10
from torch.utils.data import Subset
trainset = Subset(trainset, range(128))
testset = Subset(testset, range(128))
Run:
python main.py
Save processor / image-processor and config - If using a processor or image_processor object (from transformers), save it:
processor.save_pretrained(out_dir)
- If using a transformers config, save it similarly:
config.save_pretrained(out_dir)
Create a model card (MODEL_CARD.md) - Include: short description, dataset name + license, training hyperparameters, evaluation metrics, usage snippet. Example front matter (minimal):