Validation
Validation was run on 2026-07-31 with an Apple M5 Pro, 24 GB unified memory, macOS 26.6, Python 3.12.13, and MLX 0.32.0.
Checkpoint conversion
- Source:
Audio8/ARK-ASR-0.6B - Revision:
45776b56d58cdfb2e2eb632f7e110f38684633e0 - Retained parameter tensors: 780
- Missing or unexpected tensors: 0
- Output dtype: BF16
- Output size: 2.1 GiB
- Source SHA-256:
57a86ce1c2f2c2d6ebb7ad9642c9e951a5109625122b48c9180126a28787673d - Output SHA-256:
4f17d6fda7be51e489c1819c266ed946b2d3982368f0a231ceb0c1f211a3e56c
PyTorch parity
The original remote-code model and the 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 features cosine similarity:
0.9998097773 - Initial decoder logits cosine similarity:
0.9999750792 - 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.
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:
0.362 s - Audio preprocessing:
0.396 s - First token:
0.110 s - Full 38-token generation:
0.384 s - Generation throughput:
98.94 tokens/s - MLX peak memory:
2.620 GB
These numbers describe this machine and test clip; they are not portable performance guarantees.