Instructions to use dexay/reDs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dexay/reDs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dexay/reDs")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dexay/reDs") model = AutoModelForSequenceClassification.from_pretrained("dexay/reDs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dexay/reDs: direct link, hf CLI and curl.
- Browser
- Download file 1.46 GB
-
https://huggingface.co/dexay/reDs/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dexay/reDs/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dexay/reDs/resolve/main/pytorch_model.bin
1.46 GB
- Xet hash:
- 12fcd7ea61c4356c56d0ebf6df0cb1b44ddbb777058820aaeb9b59a36e48d740
- Size of remote file:
- 1.46 GB
- SHA256:
- 1cdf58109fd585b05c3ff8f64228744a1da07ee061daea49cc577c93b57f1ce0
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