Instructions to use Colorful/RTA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Colorful/RTA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Colorful/RTA")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Colorful/RTA") model = AutoModelForSequenceClassification.from_pretrained("Colorful/RTA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Colorful/RTA: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Colorful/RTA/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Colorful/RTA@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Colorful/RTA/resolve/refs%2Fpr%2F1/pytorch_model.bin
499 MB
- Xet hash:
- 2fd68469588a87f89d324089483cd9e702bedbf545f39ac42cf03142f6319e71
- Size of remote file:
- 499 MB
- SHA256:
- e70271261e98f7c76de2d071d92fa2fb64aa88411fa2838df5d290d6e80b10ee
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