Instructions to use leope/ark-asr-3B-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use leope/ark-asr-3B-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir ark-asr-3B-mlx leope/ark-asr-3B-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| # Validation | |
| Validation was run on 2026-08-01 with an Apple M5 Pro, 24 GB unified memory, | |
| macOS 26.6, Python 3.12.13, and MLX 0.32.0. | |
| ## Checkpoint conversion | |
| - Source: `AutoArk-AI/ARK-ASR-3B` | |
| - Revision: `1e28271b79edc97635783bea65abc89195a09ed3` | |
| - Source tensors: 926 | |
| - Intentionally dropped tensors: 2 | |
| - Retained parameter tensors: 924 | |
| - Missing, unexpected, or shape-mismatched tensors: 0 | |
| - Output dtype: BF16 | |
| - Output size: 7.0 GiB | |
| - Output SHA-256: | |
| `a5e9431bdd648340a40c092e385c4fe1d445d8ad3311dd94609e72af36b0256d` | |
| Source shard hashes are recorded in `conversion.json`. | |
| ## PyTorch parity | |
| The original remote-code model and native MLX model were run on the pinned | |
| `Narsil/asr_dummy/1.flac` LibriSpeech sample. | |
| - Prompt token IDs: identical | |
| - BF16 input features: maximum absolute difference `0.0` | |
| - Adapted audio feature cosine similarity: `0.998567558665821` | |
| - Initial decoder logits cosine similarity: `0.9999223476845636` | |
| - Greedy generation token IDs: identical | |
| - Final decoded text: identical | |
| Both implementations produced: | |
| > he hoped there would be stew for dinner turnips and carrots and bruised | |
| > potatoes and fat mutton pieces to be ladled out in thick peppered flour | |
| > fattened sauce | |
| The validation command was: | |
| ```bash | |
| python scripts/validate_parity.py /path/to/1.flac \ | |
| --model . \ | |
| --source AutoArk-AI/ARK-ASR-3B \ | |
| --revision 1e28271b79edc97635783bea65abc89195a09ed3 \ | |
| --min-adapter-cosine 0.998 | |
| ``` | |
| ## MLX benchmark | |
| The same 12.1-second audio sample was measured after the checkpoint was already | |
| present in the operating-system file cache: | |
| - Model load: `1.011 s` | |
| - Audio preprocessing: `0.499 s` | |
| - First token: `0.885 s` | |
| - Full 34-token generation: `1.965 s` | |
| - Generation throughput: `17.30 tokens/s` | |
| - MLX peak memory: `7.820 GB` | |
| These numbers describe this machine and test clip; they are not portable | |
| performance guarantees. | |