Instructions to use LDD/wwm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LDD/wwm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LDD/wwm")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LDD/wwm") model = AutoModel.from_pretrained("LDD/wwm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from LDD/wwm: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/LDD/wwm/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://LDD/wwm/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/LDD/wwm/resolve/main/pytorch_model.bin
409 MB
- Xet hash:
- 32e29e3ecb054b1e0536fe218de83472f608bf21da591f739c66af40d9f9bc9e
- Size of remote file:
- 409 MB
- SHA256:
- 5a37d8b4f6fbbec4d648642f13f56f5552952836a1799f511dcd203a5cdd13af
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