Instructions to use pandalla/ChatLaw2-MoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pandalla/ChatLaw2-MoE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="pandalla/ChatLaw2-MoE", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("pandalla/ChatLaw2-MoE", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from pandalla/ChatLaw2-MoE: direct link, hf CLI and curl.
- Browser
- Download file 129 Bytes
-
https://huggingface.co/pandalla/ChatLaw2-MoE/resolve/main/training_args.bin
- Command line
-
hf download hf://pandalla/ChatLaw2-MoE/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/pandalla/ChatLaw2-MoE/resolve/main/training_args.bin
129 Bytes
- Xet hash:
- f495ca72150c0e6cc7caf9705fde6665b9e1168aaf5d0e23309c91ce43411803
- Size of remote file:
- 129 Bytes
- SHA256:
- 6b62b3e65ee30d1d8e51245eb90c9414cc707ab9cda99c090291e44eed308f3f
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