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:
- b44269d0cb1f093dcdb7687a4e53685eb9c9dca2e29a07e546dce0b675c67c3c
- Size of remote file:
- 4.83 GB
- SHA256:
- 4fd9a103c9471c6bea610f4752dd3f2c65819dd0203b5f9aff7dc43dac4683d3
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