Instructions to use funnel-transformer/small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use funnel-transformer/small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="funnel-transformer/small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("funnel-transformer/small") model = AutoModel.from_pretrained("funnel-transformer/small", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from funnel-transformer/small: direct link, hf CLI and curl.
- Browser
- Download file 524 MB
-
https://huggingface.co/funnel-transformer/small/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://funnel-transformer/small/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/funnel-transformer/small/resolve/main/pytorch_model.bin
524 MB
- Xet hash:
- a02fad1beef9c14f685fc5e0525445c0ba7cda25cd5b9bf5a9b14313f0f0d7d6
- Size of remote file:
- 524 MB
- SHA256:
- 38728a530f6fd5b3f1459753ccb743280af0d1b18b44da0e41bf0c159e4f705a
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