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:
- 9e7ddd433039f69af4971360f9710aec9cc91bc2958c869a0089888147796957
- Size of remote file:
- 4.97 GB
- SHA256:
- 6d58a810061f3ffec4253d9e6be40991c42638e8d3852d66413bb113648b36db
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