Instructions to use YakovElm/IntelDAOS5Classic_512 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS5Classic_512 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS5Classic_512")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS5Classic_512") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS5Classic_512", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS5Classic_512: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS5Classic_512/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS5Classic_512/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS5Classic_512/resolve/main/tf_model.h5
438 MB
- Xet hash:
- ac3a27e5f9655cbf90bae20c92578871e3a923736c2497e758a5ca8c580be572
- Size of remote file:
- 438 MB
- SHA256:
- 051fe7aa7714f413d6b7e61e03037ccdc33e4d7578467a0582c18c1d057147e9
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