Instructions to use linktimecloud/my_awesome_model_classification_w_adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use linktimecloud/my_awesome_model_classification_w_adapter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="linktimecloud/my_awesome_model_classification_w_adapter")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("linktimecloud/my_awesome_model_classification_w_adapter") model = AutoModelForSequenceClassification.from_pretrained("linktimecloud/my_awesome_model_classification_w_adapter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from linktimecloud/my_awesome_model_classification_w_adapter: direct link, hf CLI and curl.
- Browser
- Download file 502 MB
-
https://huggingface.co/linktimecloud/my_awesome_model_classification_w_adapter/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://linktimecloud/my_awesome_model_classification_w_adapter/pytorch_model.bin
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curl -L -o pytorch_model.bin https://huggingface.co/linktimecloud/my_awesome_model_classification_w_adapter/resolve/main/pytorch_model.bin
502 MB
- Xet hash:
- 00484e5721ce3f0f50b7631d6bd96974985d212e097d69bd3caefc64eac3f004
- Size of remote file:
- 502 MB
- SHA256:
- 9d4e0ee0ebd3a60505e5c506cb6c78b7d6598c91605e9141da7610657556d435
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