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VoiceScribe/qwen3-5-0.8b-dictation-corrector-mlx-mixed46-g32

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face
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Qwen3.5-0.8B V15 LoRA Dictation Corrector (MLX mixed46 g=32)

Default V15 R-3 corrector for Apple Silicon with ≥16 GB unified memory. PTQ on V15's bf16-fused weights via mlx-lm --quant-predicate mixed_4_6 --q-group-size 32. Strictly Pareto-better than the V15 Q8 production mirror: smaller, lower RSS, identical short-form accuracy, better long-form WER.

Eval results — three independent angles

Eval setV15 prod (Q8 g=64)**This mirror**V14 4-bit baseline
seed_v5 wild (50 rows, hard-neg discourse)100%100%74.1%
ECHO15 long-form WER vs Gemini-3.1 ref0.2100.160not tested
Dev-corpus long-form WER vs Gemini-3.5-Flash ref0.1410.127not tested
Disk size782 MB482 MB424 MB
Warm RSS (process)1573 MB1326 MB~1100 MB
Bits/weight (avg)~8.5{5.127}~4.5

This mirror is strictly Pareto-optimal vs V15 Q8 on every axis: smaller, lower RSS, equal-or-better quality. See companion mirror `qwen3-5-0.8b-dictation-corrector-mlx-mxfp4-g32` for the M1 8 GB (8-15 GiB unified) tier.

Tier-of-use

Best for: M2 16 GB+ (16 GiB+ unified).

The voice-scribe-macos installer (WP#1074) ships both this mirror and its sibling and picks one at install time via sysctl hw.memsize:

  • —8-15 GiB → mxfp4 g=32 (this mirror, 400 MB / 1244 MB RSS)
  • —16+ GiB → mixed46 g=32 (482 MB / 1326 MB RSS)

Quickstart

python
from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler

model, tokenizer = load("VoiceScribe/qwen3-5-0.8b-dictation-corrector-mlx-mixed46-g32")

messages = [
    {"role": "system", "content": "Корректор русской диктовки. Убери слова-паразиты. Нормализуй IT-термины. Не меняй смысл."},
    {"role": "user", "content": "Эм, докер мониторит, ну, бэкенд через гитхаб экшнс"},
]
prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=False
)
sampler = make_sampler(temp=0.0, top_p=1.0)
out = generate(model, tokenizer, prompt=prompt, max_tokens=200, sampler=sampler)
print(out)  # → "Docker мониторит бэкенд через GitHub Actions"

Quantisation recipe — no retraining

Source: V15 R-3 LoRA-fused bf16 weights (the predecessor of the V15 Q8 production mirror). PTQ via mlx-lm 0.31.x:

bash
python -m mlx_lm convert \
    --hf-path /path/to/v15-r3-fused-bf16 \
    --mlx-path ./qwen3-5-0.8b-dictation-corrector-mlx-mixed46-g32 \
{    --quantize --q-mode affine --q-group-size 32 \
    --quant-predicate mixed_4_6 \
}    --dtype bfloat16

The bf16-fused weights are the canonical V15 source; the V15 Q8 production mirror was produced from the same source via uniform Q8 quantisation. This mirror simply uses a smarter quantiser on the same proven training signal.

Why PTQ (not retraining on Q4 base)

Predecessor approaches (V14, V16) attempted to train a LoRA adapter directly on a Q4-quantised base. Both produced regressions because Q4 forward gradients are too noisy — the adapter learns a sloppier signal than the bf16-base V15 path.

This mirror reverses the order: train on bf16 (proven V15 path), then quantise smarter. Result: the adapter signal is preserved faithfully; the only loss is from re-quantising weights with a more aggressive bit budget on layers where it doesn't hurt.

The {mixed_4_6 predicate (sensitive layers at 6 bit, MLP at 4 bit)} keeps the high-magnitude channels (attention output projections — where the LoRA-trained signal concentrates) at high precision while compressing the MLP feedforward layers (which tolerate aggressive quant). Result: 38-49% smaller disk + 16-21% lower RSS at zero (or better) quality vs the uniform Q8 mirror.

Intended use

  • —Yes: Russian dictation cleanup after ASR (GigaAM, Whisper, Parakeet). Removes fillers, normalises Cyrillic IT terms (докер→Docker, гитхаб→GitHub), preserves meaning verbatim.
  • —No: General text editing, English text, summarisation, translation, creative writing. Trained for a strict conservative-edit policy; will not paraphrase.

Limitations

  • —Numeric edge cases: rare numeral-word sequences may regenerate with substitution errors.
  • —OOD brand normalisation: brands not in training data may stay in Cyrillic transliteration.
  • —English-language inputs: not supported (Russian only).
  • —Small eval sample: 50 + 30 + 2 (long-form) files. Real-world variance higher than reported confidence interval.

Ethical considerations

  • —Privacy: runs entirely on-device. No telemetry, no cloud round-trip.
  • —No user data in training: all training prompts are synthetic (authored by maintainer with AI assistance). Production usage does not contribute to future training.
  • —Conservative policy: preserves exact meaning; never paraphrases.

Citation

bibtex
@software{{voicescribe-v15-mixed46-2026,
  title  = {{Voice Scribe Russian Dictation Corrector V15 R-3 (mixed46 {Apple Silicon M2 16+ tier})}},
  author = {{Sabynin, Andrey}},
  year   = {{2026}},
  note   = {{WP#1067 R&D + WP#1074 productisation}},
  url    = {{https://huggingface.co/VoiceScribe/qwen3-5-0.8b-dictation-corrector-mlx-mixed46-g32}}
}}

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