Instructions to use hfl/cino-base-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/cino-base-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hfl/cino-base-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hfl/cino-base-v2") model = AutoModelForMaskedLM.from_pretrained("hfl/cino-base-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de8caf9096781de9affc74570f0ec8a44c99fb3565b1f7b984f323fafeb59fa1
- Size of remote file:
- 761 MB
- SHA256:
- 028717bbb109b3564e3d787d532d839ce1ff300c65117c757e30db03189c8fe9
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