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 optimizer.pt from chenmingxuan/PatGlmSum: direct link, hf CLI and curl.
- Browser
- Download file 29.4 MB
-
https://huggingface.co/chenmingxuan/PatGlmSum/resolve/main/optimizer.pt
- Command line
-
hf download hf://chenmingxuan/PatGlmSum/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/chenmingxuan/PatGlmSum/resolve/main/optimizer.pt
29.4 MB
- Xet hash:
- cf7b10806ddac004726fa41b5fcf3fa4af0736aaf57f6c58fbe89ce99f9a9b86
- Size of remote file:
- 29.4 MB
- SHA256:
- 3865599e3c5376087669f9ca697cae24fe455c22d985fe29ac3ac7f13aea7ecc
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