Instructions to use YakovElm/IntelDAOS5Classic_MSE_Sample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use YakovElm/IntelDAOS5Classic_MSE_Sample with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="YakovElm/IntelDAOS5Classic_MSE_Sample")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("YakovElm/IntelDAOS5Classic_MSE_Sample") model = AutoModelForSequenceClassification.from_pretrained("YakovElm/IntelDAOS5Classic_MSE_Sample", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from YakovElm/IntelDAOS5Classic_MSE_Sample: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/YakovElm/IntelDAOS5Classic_MSE_Sample/resolve/main/tf_model.h5
- Command line
-
hf download hf://YakovElm/IntelDAOS5Classic_MSE_Sample/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/YakovElm/IntelDAOS5Classic_MSE_Sample/resolve/main/tf_model.h5
438 MB
- Xet hash:
- 66c1d155bf6b584c4cd502eae3d723413013b8c62d642c22731f23189bf08bde
- Size of remote file:
- 438 MB
- SHA256:
- cda013725f4c3e7a2283b282075b4d25442a2a53293a08b478006d06cf5a3fb7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.