Instructions to use hf-tiny-model-private/tiny-random-NezhaModel 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-NezhaModel 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-NezhaModel")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-NezhaModel", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1461177ce43fe17636bfea8cf3d232331fff5c4dfd165ef302004c35e432ee34
- Size of remote file:
- 2.92 MB
- SHA256:
- 4b5dd92750e9a777b9d33a033ce07b56b5eee299079e98328f7b2f329271df6c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.