---
pipeline_tag: text-generation
---

# RADD Small (lambda-dce)

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},
}
```