Instructions to use YakovElm/IntelDAOS15Classic_256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS15Classic_256 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS15Classic_256")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS15Classic_256") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS15Classic_256", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS15Classic_256: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS15Classic_256/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS15Classic_256/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS15Classic_256/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 4d1c77547aed0767b1b14125533a3d0156a64134b4a808e58e6ce1e94f7e8594
- Size of remote file:
- 438 MB
- SHA256:
- 58df04a001dfe23b547ea18aa7f61d76e4eaace439d375eada999f86f3d91527
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