Instructions to use rpii2023/naya_v2_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rpii2023/naya_v2_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="rpii2023/naya_v2_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("rpii2023/naya_v2_model") model = AutoModelForTokenClassification.from_pretrained("rpii2023/naya_v2_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from rpii2023/naya_v2_model: direct link, hf CLI and curl.
- Browser
- Download file 802 MB
-
https://huggingface.co/rpii2023/naya_v2_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rpii2023/naya_v2_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rpii2023/naya_v2_model/resolve/main/pytorch_model.bin
802 MB
- Xet hash:
- 583071fc98b52dc120e48ac9dfdefef047c8c5ff8e296842d02817135d154260
- Size of remote file:
- 802 MB
- SHA256:
- b33748e334f0c930ccbf7fdb3c32cd5eda722e6abb78f9a2f4afa07c902e3701
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