Instructions to use hf-internal-testing/tiny-random-Mask2FormerModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Mask2FormerModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-Mask2FormerModel")# Load model directly from transformers import AutoImageProcessor, AutoModelForMultimodalLM processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-Mask2FormerModel") model = AutoModelForMultimodalLM.from_pretrained("hf-internal-testing/tiny-random-Mask2FormerModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5791b33b634a240bfea90432a39a7e6f34c6776c271ee8ce4e5232b669b30c08
- Size of remote file:
- 47.4 MB
- SHA256:
- c63a557083788bece4d57c3a9fed7741fa15d136a25bf251275c99476fe73df6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.