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 tf_model.h5 from Colorful/RTA: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/Colorful/RTA/resolve/refs%2Fpr%2F1/tf_model.h5
- Command line
-
hf download hf://Colorful/RTA@refs/pr/1/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/Colorful/RTA/resolve/refs%2Fpr%2F1/tf_model.h5
499 MB
- Xet hash:
- 186ca3edaae190aafbf33f26a1bee12007ce9948d2c9554f07254d679beb5c62
- Size of remote file:
- 499 MB
- SHA256:
- 1a670353f7fe7018314625f037785ad36d8b430af876a219f9a0e0d74ecbb134
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.