Instructions to use dexay/reDs3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dexay/reDs3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dexay/reDs3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dexay/reDs3") model = AutoModelForSequenceClassification.from_pretrained("dexay/reDs3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dexay/reDs3: direct link, hf CLI and curl.
- Browser
- Download file 1.46 GB
-
https://huggingface.co/dexay/reDs3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dexay/reDs3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dexay/reDs3/resolve/main/pytorch_model.bin
1.46 GB
- Xet hash:
- 3bb77c7863e89d695edd106419ea0e664e69a8b60efaaddf42c75a4ee017a717
- Size of remote file:
- 1.46 GB
- SHA256:
- a5d20087d338afc63e38a9300733a31cf4ebf9f0b1a0cad43c8c60e2edf495d2
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