Instructions to use LDD/wwm-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LDD/wwm-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LDD/wwm-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("LDD/wwm-2") model = AutoModel.from_pretrained("LDD/wwm-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from LDD/wwm-2: direct link, hf CLI and curl.
- Browser
- Download file 409 MB
-
https://huggingface.co/LDD/wwm-2/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://LDD/wwm-2/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/LDD/wwm-2/resolve/main/pytorch_model.bin
409 MB
- Xet hash:
- 978667ba91d28ee14505a5f3586b178fe5a7508d07d7a4a81ed526c6251f5a07
- Size of remote file:
- 409 MB
- SHA256:
- d61d406bee352ea02bf8c9be777eee2aa799f0bb7623f4b950a79da70287638c
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