Instructions to use YakovElm/IntelDAOS10Classic_256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS10Classic_256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS10Classic_256")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS10Classic_256") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS10Classic_256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS10Classic_256: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS10Classic_256/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS10Classic_256/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS10Classic_256/resolve/main/tf_model.h5
438 MB
- Xet hash:
- d035ee2d10ed68d97df5811a62316cd797e28cd89d57a1d93216904d5acf74a2
- Size of remote file:
- 438 MB
- SHA256:
- f38c6cdf017395440d892ae19b56231426fe46df8bf2fff81b3d7af24a50b375
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