Add pipeline tag and improve model card

Hi! I'm Niels, part of the community science team at Hugging Face.

I've opened this PR to improve the model card for RADD:
- Added `pipeline_tag: text-generation` to the metadata to improve discoverability on the Hub.
- Added links to the official paper and GitHub repository.
- Included a sample usage snippet for loading the model, as documented in your repository.
- Added the BibTeX citation for researchers.

README.md

@@ -1,5 +1,38 @@
-Reparameterized Absorbing Discrete Diffusion (RADD) small model with lambda-dce loss trained for 400k iterations. 
+---
+pipeline_tag: text-generation
+---
 
-Code: https://github.com/ML-GSAI/RADD. 
+# RADD Small (lambda-dce)
 
-Paper: https://arxiv.org/abs/2406.03736. 
+This repository contains the small model checkpoint for **RADD (Reparameterized Absorbing Discrete Diffusion)**, trained with the $\lambda$-DCE loss for 400k iterations. 
+
+RADD is a discrete diffusion model designed for language modeling that characterizes time-independent conditional probabilities. This approach allows for sampling acceleration via caching strategies and unifies absorbing discrete diffusion with any-order autoregressive models (AO-ARMs).
+
+- **Paper:** [Your Absorbing Discrete Diffusion Secretly Models the Conditional Distributions of Clean Data](https://huggingface.co/papers/2406.03736)
+- **GitHub Repository:** [ML-GSAI/RADD](https://github.com/ML-GSAI/RADD)
+
+## Usage
+
+To use this model, you need to use the loading utility provided in the [official repository](https://github.com/ML-GSAI/RADD):
+
+```python
+from load_model import load_model
+
+# Load the model and noise schedule
+model, noise = load_model('JingyangOu/radd-lambda-dce', device='cuda') 
+```
+
+For more details on sampling (e.g., using the `DiffusionSampler` or `OrderedSampler`), please refer to the scripts in the GitHub repository.
+
+## 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},
+}
+```