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")# pip install -U transformers accelerate # 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
Download tf_model.h5 from hfl/cino-large-v2: direct link, hf CLI and curl.
- Browser
- Download file 2.33 GB
-
https://huggingface.co/hfl/cino-large-v2/resolve/main/tf_model.h5
- Command line
-
hf download hf://hfl/cino-large-v2/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/hfl/cino-large-v2/resolve/main/tf_model.h5
2.33 GB
- Xet hash:
- 8804ed9ce7c382477a91c9f54d08118b5b03a33c235746cfb7986c52fda866d8
- Size of remote file:
- 2.33 GB
- SHA256:
- f055387474c5bfe3e960a1cfb96bcfc396e7dd9a335e6b104f279333cd0afd7a
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