Any-to-Any
Transformers
Diffusers
Safetensors
English
llada2_moe
feature-extraction
multimodal
image-generation
image-understanding
image-editing
diffusion
Mixture of Experts
text-to-image
fp8
quantized
custom_code
Instructions to use inclusionAI/LLaDA2.0-Uni-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inclusionAI/LLaDA2.0-Uni-FP8 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("inclusionAI/LLaDA2.0-Uni-FP8", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 940f59c940a6dc760935778015c48369d79fa6c0f1bfd0857346e53b74d692f7
- Size of remote file:
- 15.3 MB
- SHA256:
- 2197aeddaf09785316673451ca6fb86dcfcfdb108972a3145d106b8fa4c927e6
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