Instructions to use YakovElm/IntelDAOS10Classic_512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS10Classic_512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS10Classic_512")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS10Classic_512") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS10Classic_512", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS10Classic_512: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS10Classic_512/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS10Classic_512/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS10Classic_512/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 89d9cf4edd8bfbcbb83fbf6d6b4adb536c6fd46748652c643bf77018d6d03cf0
- Size of remote file:
- 438 MB
- SHA256:
- e75e185d8f41beeff2fd7cb4c2e14564b06a03f6300e9479a5d6de0ff081c68c
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