Instructions to use hf-internal-testing/tiny-random-clip with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-clip with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-clip", dtype="auto") - Notebooks
- Google Colab
- Kaggle
V4.22.0 processor update
#2
by lewtun HF Staff - opened
This PR follows #1 and:
- updates the CLIP model to be compatible with
transformersv4.22. The previous version throws an error when trying to load the tokenizer (requiresfrom_slow=True) - sets the image and crop size to the default value of 30 associated with the checkpoint this model was derived from
cc @ydshieh
lewtun changed pull request title from V4.22.0 update to V4.22.0 processor update
The change in tokenizer_config.jsonseems strange to me. It contains crop_size, size, processor_class. I guess it is somehow mixing the fields from preprocessor_config.json?
I generated tokenizer._config.json by using CLIPProcessor.push_to_hub() - presumably these fields are added automatically?
Quick ping on this PR, I encountered this issue while running some TF tests. Is it okay to merge?