Instructions to use hf-internal-testing/tiny-random-MambaModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MambaModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-MambaModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MambaModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-MambaModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b57ac64788556694b85d2ed5894b1e343c965297e7acc9792f120f9563c76b4
- Size of remote file:
- 173 kB
- SHA256:
- 388c38e3e28b9cb3dc1d2916af8c260c3b43d54e1fc392a3d06c2d3bf3874d39
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.