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 rng_state.pth from Colorful/RTA: direct link, hf CLI and curl.
- Browser
- Download file 14.7 kB
-
https://huggingface.co/Colorful/RTA/resolve/main/rng_state.pth
- Command line
-
hf download hf://Colorful/RTA/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/Colorful/RTA/resolve/main/rng_state.pth
14.7 kB
- Xet hash:
- 121aebd11a1f470110fa4517b69dfe83e9d6a689f91dc97aa4d1878d14f0fc29
- Size of remote file:
- 14.7 kB
- SHA256:
- b280e5e7361863f1198eddffaacc5a914f07acd10dc9d4ed782383fb79916b47
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.