Instructions to use tarsur909/LLaDA-8B-Base-JSON-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tarsur909/LLaDA-8B-Base-JSON-SFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tarsur909/LLaDA-8B-Base-JSON-SFT", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tarsur909/LLaDA-8B-Base-JSON-SFT", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 40f5fefd29eeb1a33e871d6fd679aa1cf74503244e40cee7bac4bfe7d45abd18
- Size of remote file:
- 2.54 GB
- SHA256:
- 3026866ef2f9d9c2fccb90bca92884a1dbead6b82ea5a4f235d49d764a575a5a
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