Update README.md

README.md

@@ -1,3 +1,62 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- diffusion
+- discrete
+- image-generation
+- text-generation
+library_name: transformers
+inference: false
+datasets:
+- uoft-cs/cifar10
+- jiachenlei/imagenet
+- Skylion007/openwebtext
+metrics:
+- perplexity
+base_model:
+- kuleshov-group/mdlm-owt
+---
+
+# MDM-Prime
+
+**MDM-Prime** is a discrete diffusion model enhanced with the Partial masking scheme (Prime). It enables fine-grained denoising and improves generation quality across both image and text domains. This model was first proposed in our paper [*Beyond Masked and Unmasked: Discrete Diffusion Models via Partial Masking*](https://arxiv.org/abs/2505.18495).
+
+---
+
+## Model Details
+
+- **Text Generation**
+  - Dataset: openwebtext (OWT)
+  - Model Size: 130M
+  - Context Length: 1,024
+- **Image Synthesis**
+  - Dataset: CIFAR-10, ImageNet-32
+  - Model Size: 114M
+  - Context Length: 32x32x3
+
+---
+
+## How to Use
+
+```python
+from transformers import AutoModel
+
+model = AutoModel.from_pretrained('chen-hao-chao/mdm-prime')
+```
+
+For more details, please refer to our github repository: [chen-hao-chao/mdm-prime](https://github.com/chen-hao-chao/mdm-prime).
+
+---
+
+## BibTeX Entry and Citation Information
+
+If you find this code implementation useful, please consider citing our paper.
+
+```bib
+@article{chao2025mdmprime,
+      title={{Beyond Masked and Unmasked: Discrete Diffusion Models via Partial Masking}}, 
+      author={Chen-Hao Chao, Wei-Fang Sun, Hanwen Liang, Chun-Yi Lee, Rahul G. Krishnan},
+      journal={\tt arXiv:2505.18495 [cs.LG]},
+      year={2025},
+}
+```