Instructions to use maple-research-lab/LLaDOU-v0-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maple-research-lab/LLaDOU-v0-Math with Transformers:
# Load model directly from transformers import LLaDOUModelLM model = LLaDOUModelLM.from_pretrained("maple-research-lab/LLaDOU-v0-Math", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| base_model: | |
| - GSAI-ML/LLaDA-8B-Instruct | |
| language: | |
| - en | |
| library_name: transformers | |
| # Large Language Diffusion with Ordered Unmasking (LLaDOU) | |
| <a href="https://arxiv.org/abs/2505.10446"><img src="https://img.shields.io/badge/arXiv-2505.10446-b31b1b.svg" alt="ArXiv"></a> | |
| <a href="https://arxiv.org/abs/2505.10446"><img src="https://img.shields.io/badge/GitHub-LLaDOU-777777.svg" alt="ArXiv"></a> | |
| We introduce the **L**arge **La**nguage **D**iffusion with **O**rdered **U**nmasking (**LLaDOU**), which is trained by reinforcing a new reasoning paradigm named the **D**iffusion **C**hain **o**f **L**ateral **T**hought (**DCoLT**) for diffusion language models. | |
| Compared to standard CoT, DCoLT is distinguished with several notable features: | |
| - **Bidirectional Reasoning**: Allowing global refinement throughout generations with bidirectional self-attention masks. | |
| - **Format-Free Reasoning**: No strict rule on grammatical correctness amid its intermediate steps of thought. | |
| - **Nonlinear Generation**: Generating tokens at various positions in different steps. | |
|  | |
| ## Instructions | |
| **LLaDOU-v0-Math** is a math-specific model trained on GSM8K and MATH. | |
| For inference codes and detailed instructions, please refer our github page: [maple-research-lab/LLaDOU](https://github.com/maple-research-lab/LLaDOU). |