Instructions to use YakovElm/IntelDAOS20Classic_64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS20Classic_64 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS20Classic_64")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS20Classic_64") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS20Classic_64", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS20Classic_64: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS20Classic_64/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS20Classic_64/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS20Classic_64/resolve/main/tf_model.h5
438 MB
- Xet hash:
- a7be398c32f81c3b75a6fa7c45cd35d2e0c7a5000c8acfd8477ebdece2283b21
- Size of remote file:
- 438 MB
- SHA256:
- 47f41d2ffb12cb90e4f9ddefc0be86de14c6251919c4f5ec214c466532e83628
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