Instructions to use hf-internal-testing/tiny-random-BitModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-BitModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="hf-internal-testing/tiny-random-BitModel")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-BitModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-BitModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 47b8f876c33918f4ce01a72e079326f77b41432a78a30ba9d53dd96f42972782
- Size of remote file:
- 99.9 kB
- SHA256:
- 83f36e119c62dff60c55179a4c934369c044fe4fc2ad9aee1e54c593a2177da9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.