Instructions to use JakeOh/LLaDA-Tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JakeOh/LLaDA-Tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="JakeOh/LLaDA-Tiny", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JakeOh/LLaDA-Tiny", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c36d99288aafe9fd778d6a07bf324c10c62d0c3f68c03afbbdb2301a83d995aa
- Size of remote file:
- 70.9 MB
- SHA256:
- c8afb95bc7f3fec4b17baa67c0ae4849d6ca99c02b000be655f20dbd05603c99
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