Instructions to use hf-internal-testing/tiny-random-NezhaModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-NezhaModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-NezhaModel")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-NezhaModel", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8dddfbdea100757024cedbf0013d3431245812a81386b0e5634c9c2f8bff4bad
- Size of remote file:
- 2.94 MB
- SHA256:
- 0a2857922184aa66b3c29c37235b422d0f4b6f9ce7dd9f9cddd687dd424c3a38
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.