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:
- 789551b65bcd89df1c2369f1c15d650ed9f7e5564d5af20a49a855ac49a8316a
- Size of remote file:
- 4.96 GB
- SHA256:
- d8fa876e01b9b693549878563f49894ac9c47872d8ae6a696849a13fe0ccfa23
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