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:
- b2eaeda79b4cf5e9f3cb0adeca6d4584d84026a802fd7140fb142a3cd97786b1
- Size of remote file:
- 4.97 GB
- SHA256:
- 9a903fae653429452a97d32396fbf29819ce347ae19b188a95b1a06822670a35
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