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:
- 569369be19ff62ecccb907174a9c26182167b9076553e6cf24dcdf8c80d27731
- Size of remote file:
- 4.96 GB
- SHA256:
- 5423d166d9acb93259ee4319180a81e14766ea2100c8a16821c81ac9ff1b2d80
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