Instructions to use diffusion-reasoning/LLaDA-8B-Instruct-wd1-acecode-iter180 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use diffusion-reasoning/LLaDA-8B-Instruct-wd1-acecode-iter180 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="diffusion-reasoning/LLaDA-8B-Instruct-wd1-acecode-iter180", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("diffusion-reasoning/LLaDA-8B-Instruct-wd1-acecode-iter180", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 004b4aa3944e1fcef8c2979534772336c19a504e2761d315d398d30aa0d1bede
- Size of remote file:
- 4.83 GB
- SHA256:
- 590f300dd436bc0e1d1b34d7e3b948ac10ff29cc7f6d4c965342facd64a18729
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