Add RE-USE MLX converted weights

Includes the converted MLX safetensors artifact, source revision manifest,
NVIDIA noncommercial license text, attribution notice, and model card.

LICENSE

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+NVIDIA License
+
+1. Definitions
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NOTICE

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+These artifacts are converted MLX-format derivative weights for NVIDIA RE-USE.
+
+Original model: https://huggingface.co/nvidia/RE-USE
+Original license: NVIDIA One-Way Noncommercial License (NSCLv1)
+License text: https://raw.githubusercontent.com/NVlabs/HMAR/main/LICENSE
+
+This repository is not affiliated with, sponsored by, or endorsed by NVIDIA.
+NVIDIA trademarks are referenced only for attribution and license notice purposes.

README.md

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 license: other
 license_name: nvidia-one-way-noncommercial-license-nsclv1
 license_link: https://github.com/NVlabs/HMAR/blob/main/LICENSE
+track_downloads: true
+pipeline_tag: audio-to-audio
+tasks:
+  - audio-to-audio
+base_model: nvidia/RE-USE
+library_name: mlx
+tags:
+  - speech-enhancement
+  - universal speech enhancement
+  - multiple input sampling rates
+  - language-agnostic
+  - audio-to-audio
+  - mamba
+  - re-use
+  - mlx
+  - mlx-swift
+  - safetensors
+  - apple
 ---
+
+# RE-USE MLX Converted Weights
+
+This repository contains MLX-compatible converted weights for [`nvidia/RE-USE`](https://huggingface.co/nvidia/RE-USE), NVIDIA's multilingual universal speech enhancement model.
+
+These artifacts are intended for Apple Silicon / MLX / MLX Swift inference workflows.
+
+## Files
+
+| File | Description |
+|---|---|
+| `model_mlx.safetensors` | Converted MLX-compatible runtime weights. |
+| `conversion-manifest.json` | Source revision, checksum, size, license, and conversion metadata. |
+| `LICENSE` | Copy of the NVIDIA One-Way Noncommercial License (NSCLv1). |
+| `NOTICE` | Attribution and repository relationship notice. |
+
+## Source Model
+
+These weights are derived from:
+
+- Model: [`nvidia/RE-USE`](https://huggingface.co/nvidia/RE-USE)
+- Source revision: `761905064ea1ea882e015e20a64e2e9d28458890`
+- Source file: `model.safetensors`
+- Upstream model version: `30USEMamba_peak+GAN_tel_mic_1134k`
+
+This repository contains a converted derivative artifact only. It does not contain the original NVIDIA checkpoint.
+
+## Conversion
+
+The conversion changes tensor keys and layouts for MLX Swift compatibility. It does not train, fine-tune, distill, quantize, or otherwise modify the learned model behavior.
+
+The conversion consists of:
+
+- mapping source PyTorch-style parameter names to MLX Swift parameter names
+- converting convolution tensor layouts where required by the MLX implementation
+- preserving required tensor dtypes for runtime correctness
+- writing the result as `model_mlx.safetensors`
+
+See `conversion-manifest.json` for checksum and source metadata.
+
+## License / Terms of Use
+
+These converted weights are distributed under the **NVIDIA One-Way Noncommercial License (NSCLv1)**.
+
+Use is limited to **non-commercial research and educational purposes only**.
+
+The complete license text is included in this repository as `LICENSE` and is also available from NVIDIA at:
+
+<https://github.com/NVlabs/HMAR/blob/main/LICENSE>
+
+Any source code used with these weights may be licensed separately. That source-code license does not change the license terms of these converted model weights.
+
+## Attribution
+
+Original model and checkpoint are by NVIDIA and the upstream RE-USE contributors.
+
+This repository is not affiliated with, sponsored by, or endorsed by NVIDIA. NVIDIA trademarks are referenced only for attribution and license notice purposes.
+
+## Use Case
+
+Researchers and general users can use these MLX-compatible converted weights to enhance the quality of their speech data.
+
+These weights are derived from `nvidia/RE-USE` and remain subject to the NVIDIA One-Way Noncommercial License (NSCLv1).
+
+## Model Architecture
+**Architecture Type:** Convolutional encoder, Convolutional decoder, and Mamba for time–frequency modeling <br>
+**Network Architecture:** Bi-directional Mamba with 30 layers <br>
+**Number of model parameters:** 9.6M <br>
+
+## Input
+
+Input Type(s): Audio <br>
+Input Format(s): Typically `.wav` files in compatible implementations <br>
+Input Parameters: One-Dimensional (1D) <br>
+Other Properties Related to Input: 8000 Hz - 48000 Hz Mono-channel Audio <br>
+
+## Output
+
+Output Type(s): Audio <br>
+Output Format: Typically `.wav` files in compatible implementations <br>
+Output Parameters: One-Dimensional (1D) <br>
+Other Properties Related to Output: 8000 Hz - 48000 Hz Mono-channel Audio <br>
+
+## Software Integration
+
+These converted weights are intended for MLX-compatible implementations, especially MLX Swift on Apple Silicon.
+
+Upstream NVIDIA RE-USE was originally documented for NVIDIA GPU-accelerated systems. See the original model card for upstream environment, hardware, and inference instructions:
+
+<https://huggingface.co/nvidia/RE-USE>
+
+## Training Data
+
+This repository does not modify or retrain the model.
+
+Training dataset details are documented by NVIDIA in the upstream RE-USE model card:
+
+<https://huggingface.co/nvidia/RE-USE>
+
+## Checksum
+
+`model_mlx.safetensors`
+
+```text
+SHA-256: d1158502eaf39d0b11d097177160ce3804454653c5d14d17921b6c274ca53237
+Size:    38583628 bytes
+```
+
+## Ethical Considerations
+
+Speech enhancement can alter evidence-like audio. Enhanced outputs should not be represented as untouched originals.
+
+Users should evaluate model behavior on their own data and use case before deployment. For upstream NVIDIA model quality, safety, or security concerns, see the original RE-USE model card.
+
+## Citation
+
+Please cite the original NVIDIA RE-USE model and paper when using these converted weights.
+
+Original model repository:
+
+<https://huggingface.co/nvidia/RE-USE>
+
+```bibtex
+@article{fu2026rethinking,
+  title={Rethinking Training Targets, Architectures and Data Quality for Universal Speech Enhancement},
+  author={Fu, Szu-Wei and Chao, Rong and Yang, Xuesong and Huang, Sung-Feng and Zezario, Ryandhimas E and Nasretdinov, Rauf and Juki{\'c}, Ante and Tsao, Yu and Wang, Yu-Chiang Frank},
+  journal={arXiv preprint arXiv:2603.02641},
+  year={2026}
+}
+```
+
+If you use this MLX conversion artifact directly, please also cite this repository:
+
+```bibtex
+@misc{cagataycalli2026reusemlx,
+  title        = {RE-USE MLX Converted Weights},
+  author       = {Çağatay Çallı},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/faraday/re-use-mlx}},
+  note         = {MLX-compatible converted weights derived from NVIDIA RE-USE}
+}
+```

conversion-manifest.json

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+{
+  "artifact": "re-use-mlx",
+  "artifact_version": "1.0.0",
+  "source_model": "nvidia/RE-USE",
+  "source_revision": "761905064ea1ea882e015e20a64e2e9d28458890",
+  "source_file": "model.safetensors",
+  "license": "NVIDIA One-Way Noncommercial License (NSCLv1)",
+  "converter": {
+    "name": "MLX-Swift weight conversion",
+    "output_format": "MLX-compatible safetensors"
+  },
+  "files": {
+    "model_mlx.safetensors": {
+      "role": "runtime_model_weights",
+      "sha256": "d1158502eaf39d0b11d097177160ce3804454653c5d14d17921b6c274ca53237",
+      "size_bytes": 38583628
+    }
+  }
+}

model_mlx.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d1158502eaf39d0b11d097177160ce3804454653c5d14d17921b6c274ca53237
+size 38583628