Instructions to use chenmingxuan/PatGlmSum with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chenmingxuan/PatGlmSum with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="chenmingxuan/PatGlmSum", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("chenmingxuan/PatGlmSum", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.model from chenmingxuan/PatGlmSum: direct link, hf CLI and curl.
- Browser
- Download file 1.02 MB
-
https://huggingface.co/chenmingxuan/PatGlmSum/resolve/main/tokenizer.model
- Command line
-
hf download hf://chenmingxuan/PatGlmSum/tokenizer.model
-
curl -L -o tokenizer.model https://huggingface.co/chenmingxuan/PatGlmSum/resolve/main/tokenizer.model
1.02 MB
- Xet hash:
- 2d298e57b6acbc61ddd46b042c2a2c03b42becb9a074e65f7c12bf482ae12af9
- Size of remote file:
- 1.02 MB
- SHA256:
- e7dc4c393423b76e4373e5157ddc34803a0189ba96b21ddbb40269d31468a6f2
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