Instructions to use fagner/envoy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fagner/envoy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="fagner/envoy")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("fagner/envoy") model = AutoModelForTokenClassification.from_pretrained("fagner/envoy", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from fagner/envoy: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/fagner/envoy/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://fagner/envoy/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/fagner/envoy/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 4ee6cc1f025ddd5aa7c86adf547b2dab9a07571a8bea7ecd3aa323bd6b9a6e5f
- Size of remote file:
- 431 MB
- SHA256:
- 8c0de00884e3be0723c9ae9e5d6880db22290f31d8eaddd7c82fedd8d93daa90
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