# Attribution — please retain with the weights

**The original model is ARK-ASR by AutoArk. The research authors are
Yu Lin, Yiming Wang, Runyuan Cai, and Xiaodong Zeng.**

- Original checkpoint: [Edge0/ARK-ASR-0.6B](https://huggingface.co/Edge0/ARK-ASR-0.6B)
- Original training, architecture and evaluation code: [AutoArk/open-audio-opd](https://github.com/AutoArk/open-audio-opd)
- Paper: [Data-Efficient On-Policy Distillation for Automatic Speech Recognition](https://arxiv.org/abs/2605.28139)
- Pinned source revision: `45776b56d58cdfb2e2eb632f7e110f38684633e0`
- Original weights license: Apache-2.0, retained in [LICENSE](LICENSE).

**The GGUF conversion builds on harshav's existing ARK converter and runtime,**
published with [ARK-ASR-3B-GGUF](https://huggingface.co/harshav/ARK-ASR-3B-GGUF).
The underlying native inference engine is the work of
[the transcribe.cpp authors](https://github.com/handy-computer/transcribe.cpp).
Their complete MIT license and copyright notice are retained in
[CONVERTER-LICENSE](CONVERTER-LICENSE). The reused archive and its checksum
are recorded in [conversion.json](conversion.json).

**maxffarrell's contribution is the 0.6B adaptation, quantization, validation
and packaging**, with Codex assistance. It is a community format conversion,
without retraining or original-author endorsement. It does not claim authorship
of the original model, research, training, or reused converter/runtime.

The v2 GGUF embeds these credits, license and source links in its metadata.
Please retain this file, NOTICE and both license files when redistributing.

## Original research citation

```bibtex
@misc{lin2026dataefficientopd,
  title={Data-Efficient On-Policy Distillation for Automatic Speech Recognition},
  author={Lin, Yu and Wang, Yiming and Cai, Runyuan and Zeng, Xiaodong},
  year={2026},
  eprint={2605.28139},
  archivePrefix={arXiv},
  primaryClass={cs.AI},
  url={https://arxiv.org/abs/2605.28139}
}
```