Instructions to use hfl/cino-large-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hfl/cino-large-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hfl/cino-large-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hfl/cino-large-v2") model = AutoModelForMaskedLM.from_pretrained("hfl/cino-large-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8d41c8c7bafb0ff5ffe2eea91c59003d84e34953a0a79d2268a6ee04c08bb9b9
- Size of remote file:
- 1.77 GB
- SHA256:
- 4a31d64ceac61709bddfa01cd88d700dae28a73845bc2e7be61840ab12455ce7
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