README.md

---
pipeline_tag: text-generation
---

# RADD-Medium (lambda-dce)

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},
}
```