Instructions to use xihajun/50_pruned_optimize_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xihajun/50_pruned_optimize_model with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" 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("question-answering", model="xihajun/50_pruned_optimize_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("xihajun/50_pruned_optimize_model") model = AutoModelForQuestionAnswering.from_pretrained("xihajun/50_pruned_optimize_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from xihajun/50_pruned_optimize_model: direct link, hf CLI and curl.
- Browser
- Download file 926 MB
-
https://huggingface.co/xihajun/50_pruned_optimize_model/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://xihajun/50_pruned_optimize_model/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/xihajun/50_pruned_optimize_model/resolve/main/pytorch_model.bin
926 MB
- Xet hash:
- f6eae24658d9afe85427312574921ec8bc34d846456698aac9e1d33e3c0975fc
- Size of remote file:
- 926 MB
- SHA256:
- 760f42051fb41a3670adfca875b4efb30a5bfbd3c8e788c890b3ea33adf99995
路
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