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:
- 39d1cd16b19d9c9701d2f3f522122ccf5e5b38cc84d3c0048a106666a74fa109
- Size of remote file:
- 318 kB
- SHA256:
- 406ef9b141e12240c0a45f612d9bf235c30938694faf04e01dc57daa0b7d82cd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.