Instructions to use hf-internal-testing/tiny-random-FunnelModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-FunnelModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-FunnelModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-FunnelModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-FunnelModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5206f1401f487b3a3350cdef52ae992485b45e81b4d39a4c9de5aa76d45816a8
- Size of remote file:
- 314 kB
- SHA256:
- 82cfdd3dc9ab90a2c690635119fa160da2d5b9344257bd6403a0f1e855466e01
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.