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README.md

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+---
+license: mit
+pipeline_tag: text-generation
+---
+
+# ELF: 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:** [GitHub](https://github.com/lillian039/ELF)
+
+## Evaluation and Inference
+
+The official implementation is provided in JAX. To evaluate the pre-trained checkpoints, follow the installation instructions in the [GitHub repository](https://github.com/lillian039/ELF) and use the following commands from the `src/` directory:
+
+### Unconditional Generation (OpenWebText)
+```bash
+python eval.py \
+    --config configs/training_configs/train_owt_ELF-B.yml \
+    --checkpoint_path embedded-language-flows/ELF-B-owt
+```
+
+### Conditional Generation (Summarization/Translation)
+```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}
+}
+```