Instructions to use Cube/cubetest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cube/cubetest with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Cube/cubetest")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Cube/cubetest") model = AutoModelForMaskedLM.from_pretrained("Cube/cubetest", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Cube/cubetest: direct link, hf CLI and curl.
- Browser
- Download file 530 MB
-
https://huggingface.co/Cube/cubetest/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Cube/cubetest/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Cube/cubetest/resolve/main/pytorch_model.bin
530 MB
- Xet hash:
- f949965a675f71c43a8c1ee073055f31b3372346605e6de4515618c3131d20ec
- Size of remote file:
- 530 MB
- SHA256:
- aa08e8b798d816104b8ac5bb89cdc042473a9d80775c605c223b230072326e26
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