Instructions to use diffusion-reasoning/LLaDA-8B-Instruct-wd1-acecode-iter100 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-iter100 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-iter100", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("diffusion-reasoning/LLaDA-8B-Instruct-wd1-acecode-iter100", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b3ab03508613514af61107aaf5fa714a3b9f9ef89f75793445ac720974f4ce94
- Size of remote file:
- 4.83 GB
- SHA256:
- 5f051201d9abccd6d1661a027da39bf8e2b60ca5354fe24da5dd41561be74577
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