Text Classification
Transformers
PyTorch
English
modernbert
encoder
decision-model
tool-routing
agentic
Instructions to use open-zzrl/kyo-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use open-zzrl/kyo-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="open-zzrl/kyo-instruct")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("open-zzrl/kyo-instruct", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from open-zzrl/kyo-instruct: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/open-zzrl/kyo-instruct/resolve/main/tokenizer.json
- Command line
-
hf download hf://open-zzrl/kyo-instruct/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/open-zzrl/kyo-instruct/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- 95ce659880387cbfd72ada273350d3e7d3717ae371fd87b1ab0c8a880e9a258f
- Size of remote file:
- 34.4 MB
- SHA256:
- 4d2bcb6a0bffcf6262bebc75b79783f6d85833ccea014853a5d24188c2839e11
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