README.md

---
license: mit
pipeline_tag: text-generation
---

# ELF: Embedded Language Flows

Embedded Language Flows (ELF) is a class of continuous diffusion language models based on continuous-time Flow Matching. Unlike existing diffusion language models (DLMs), ELF predominantly stays within the continuous embedding space until the final time step, where it maps to discrete tokens using a shared-weight network. This formulation makes it straightforward to adapt established techniques from image-domain diffusion models, such as classifier-free guidance (CFG).

- **Paper:** [ELF: Embedded Language Flows](https://huggingface.co/papers/2605.10938)
- **Repository:** [https://github.com/lillian039/ELF](https://github.com/lillian039/ELF)

## Description

Experiments show that ELF substantially outperforms leading discrete and continuous DLMs, achieving better generation quality with fewer sampling steps. These results suggest that ELF offers a promising path toward effective continuous DLMs.

## Usage

To use these models, please follow the installation instructions in the [official repository](https://github.com/lillian039/ELF). 

### Inference and Evaluation

You can run evaluation for unconditional generation (e.g., using ELF-B) with the following command:

```bash
cd src/

python eval.py \
    --config configs/training_configs/train_owt_ELF-B.yml \
    --checkpoint_path embedded-language-flows/ELF-B-owt
```

For conditional tasks like translation or summarization, use the corresponding configuration files:

```bash
# XSum (summarization)
python eval.py \
    --config configs/training_configs/train_xsum_ELF-B.yml \
    --checkpoint_path embedded-language-flows/ELF-B-xsum

# WMT14 De-En (translation)
python eval.py \
    --config configs/training_configs/train_de-en_ELF-B.yml \
    --checkpoint_path embedded-language-flows/ELF-B-de-en
```

## Citation

```bibtex
@article{elf2026,
  title={ELF: Embedded Language Flows},
  author={Hu, Keya and Qiu, Linlu and Lu, Yiyang and Zhao, Hanhong and Li, Tianhong and Kim, Yoon and Andreas, Jacob and He, Kaiming},
  journal={arXiv preprint arXiv:2605.10938},
  year={2026}
}
```