Add pipeline tag, sample usage and paper/code links

Hi! I'm Niels from the Hugging Face community science team.

This pull request improves the model card by:
- Adding the `text-generation` pipeline tag to the metadata.
- Including a link to the research paper: "Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data".
- Providing a sample usage code snippet from the official repository to help users load the model.
- Adding the BibTeX citation from the paper.

README.md

@@ -1,7 +1,38 @@
-Reparameterized Absorbing Discrete Diffusion (RADD) medium model with lambda-dce loss trained for 400k iterations.
+---
+pipeline_tag: text-generation
+---
 
-Code: https://github.com/ML-GSAI/RADD.
+# RADD-Medium (lambda-dce)
 
-Paper: https://arxiv.org/abs/2406.03736.
+Reparameterized Absorbing Discrete Diffusion (RADD) medium model with lambda-dce loss trained for 400k iterations. 
 
+This model was introduced in the paper [Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data](https://huggingface.co/papers/2406.03736).
 
+## Model Description
+RADD is a dedicated diffusion model without time-condition that characterizes time-independent conditional probabilities. This architecture unifies training objectives for absorbing discrete diffusion and any-order autoregressive models (AO-ARMs). The removal of the time condition allows for caching strategies that significantly improve sampling speed. This specific checkpoint is the **medium** version (approx. 405M parameters) trained using the **$\lambda$-DCE** loss function.
+
+## Links
+- **Paper:** [Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data](https://arxiv.org/abs/2406.03736)
+- **Code:** [Official GitHub Repository](https://github.com/ML-GSAI/RADD)
+
+## Sample Usage
+To load the model and noise schedule, you can use the following code (requires the `load_model.py` script from the official [GitHub repository](https://github.com/ML-GSAI/RADD)):
+
+```python
+from load_model import load_model
+
+# Load model and noise schedule
+model, noise = load_model('JingyangOu/radd-lambda-dce-medium', device='cuda') 
+```
+
+## Citation
+```bibtex
+@misc{ou2024absorbingdiscretediffusionsecretly,
+      title={Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data}, 
+      author={Jingyang Ou and Shen Nie and Kaiwen Xue and Fengqi Zhu and Jiacheng Sun and Zhenguo Li and Chongxuan Li},
+      year={2024},
+      eprint={2406.03736},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+}
+```