Instructions to use YakovElm/IntelDAOS10Classic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS10Classic with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS10Classic")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS10Classic") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS10Classic", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS10Classic: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS10Classic/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS10Classic/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS10Classic/resolve/main/tf_model.h5
438 MB
- Xet hash:
- c9e5a83a5e64458e14baccfb70e953c710b944819967f06317e01763eb9c4500
- Size of remote file:
- 438 MB
- SHA256:
- 3e75f7de3e3cf4bd2c119ada3862a5743c9f58e8677b9096e7ea31ad820cdbfd
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