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:
- 94dfc98325bc85a0f0d5a02d0bb5ce76c68a37a4f4cbdba613f92479dbec2688
- Size of remote file:
- 173 kB
- SHA256:
- 72ee3ceb46eda7e940d94a7d9b4695479651b51973edacea660b7376dc77c930
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.