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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9cb91d1419be7a75b3b8e60a8779fc22b9dcc76d59416d5f0522039d09968ae4
- Size of remote file:
- 318 kB
- SHA256:
- e40f8b18c01b3f38bf5609175254867ae5d447a6556f2c75043b3fdf0075024a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.