Instructions to use microsoft/unihanlm-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/unihanlm-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="microsoft/unihanlm-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/unihanlm-base") model = AutoModel.from_pretrained("microsoft/unihanlm-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/unihanlm-base: direct link, hf CLI and curl.
- Browser
- Download file 705 MB
-
https://huggingface.co/microsoft/unihanlm-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/unihanlm-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/unihanlm-base/resolve/main/pytorch_model.bin
705 MB
- Xet hash:
- 178db4d4338e8fe59135a4e8c55053f28d0c7da32d1887d67c7b949b2682f02b
- Size of remote file:
- 705 MB
- SHA256:
- da6d48b3711b418b1d8cbc3ca5de252bfa24c5a423d3354561e2051afd3cfc54
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