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:
- 0141059b4cedd09e1c791fc22647d4336bead8cdf5451c7581bf638af6b31a53
- Size of remote file:
- 4.97 GB
- SHA256:
- cc173e025ffbbd4b8bca2184089ca0a0c89643dc8211b1a99c315c810996cbba
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