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:
- 2fd2e7f6d6ab8b07529cdbdff302d691c1b514cf75f8555d850beaf72e397871
- Size of remote file:
- 4.83 GB
- SHA256:
- 6e64f7bff36bf3e6880b06447cc1c4fd1f874e5bf5f2198e92e434e480d7fb93
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