Instructions to use hf-internal-testing/tiny-random-RwkvModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RwkvModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-RwkvModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-RwkvModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-RwkvModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 75af170e1d84799c90642bb9b53211e14b583dbb043833f9662c3c79477ee092
- Size of remote file:
- 297 kB
- SHA256:
- 16406fc3631da9d27104bb226788ee231ca347dd5559a50f4ea51e72b0e4ea20
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.