Instructions to use hfl/cino-small-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/cino-small-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hfl/cino-small-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hfl/cino-small-v2") model = AutoModelForMaskedLM.from_pretrained("hfl/cino-small-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 11d261ec628ba7f740442874dd3bf548754dd0519a3cf474aa6cfe6e65033ecb
- Size of remote file:
- 590 MB
- SHA256:
- e7ee71d00df93389fd1b1d338b41e8ee43e4889c6ec8689e2c8cda6b1203793f
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