Instructions to use hf-tiny-model-private/tiny-random-SwitchTransformersModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-SwitchTransformersModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-SwitchTransformersModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-SwitchTransformersModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-SwitchTransformersModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 57dcd91db6dcbd49e197723d42ebb4288449e0e9a12a1346943e4ef9969f921b
- Size of remote file:
- 4.47 MB
- SHA256:
- 0576e399a9a72864ed9798dfe80209d3df2c25b7f93375ff0832cab38cb40041
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.