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majentik/MERaLiON-3-3B-ASR-MLX-8bit
MERaLiON-3-3B-ASR-MLX-8bit is a automatic speech recognition model from majentik. Use it when you need speech turned into text. It is set up for mlx. The card lists the license as other.
MLX affine + MLX (8-bit) variant of MERaLiON/MERaLiON-3-3B-ASR.
Downloads · 30 days
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.safetensors4.2 GB · 99%
How the weights are stored.
U322.6B · 79%
From the Hugging Face model README
MLX affine + MLX (8-bit) variant of MERaLiON/MERaLiON-3-3B-ASR.
Highest-quality MLX variant at ~4.2 GB. Closest to upstream BF16 quality; pick on 16+ GB Macs when accuracy is paramount.
| Device | VRAM | Recommendation |
|---|---|---|
| Apple Silicon (8 GB) | ~4.5 GB | Marginal; 16+ GB preferred |
| Apple Silicon (16+ GB) | ~4.5 GB | Highest quality MLX variant |
python3 -m pipelines.mlx_direct_quantize \
--model meralion3-3b-asr \
--bits 8 --mode affine --group-size 64 \
--base-dir ./MERaLiON-3-3B-ASR-bf16 \
--out-dir ./MERaLiON-3-3B-ASR-MLX-8bit
Reproduced at commit 149d7b6871b8e158c4bfd81b537a324beafca10a.
This is an ASR (audio-text) model, so text-benchmark suites do not apply. Its measured quality metric is character error rate (CER) against an MLX-BF16 self-reference — see the Audio inference smoke section below (gating clips: Hokkien CER 0.01, Singlish CER 0.0; threshold ≤ 0.05; PASS on 2026-07-02).
PASS — this variant passed the audio-inference smoke gate on 2026-07-02.
Gate design: character error rate (CER) is measured against an MLX-BF16 self-reference baseline (the same MLX audio path run at BF16 — not the archived torch reference, which is unstable on 2 of the 4 clips). Only the stable gating clips (2_hokkien.wav, 4_codeswitch_singlish.wav — Hokkien and Singlish) decide the verdict; the other clips are transcribed and recorded as informational. Threshold: CER ≤ 0.05 on every gating clip.
| clip | CER vs MLX-BF16 | gating | wall_s |
|---|---|---|---|
1_cantonese.wav | 3.0 | no | 0.534 |
2_hokkien.wav | 0.01 | yes | 1.01 |
3_codeswitch_tamil.wav | 0.796875 | no | 0.433 |
4_codeswitch_singlish.wav | 0.0 | yes | 0.235 |
pipelines/meralion3_mlx package (driver: pipelines/meralion3_mlx_smoke.py).149d7b6871b8e158c4bfd81b537a324beafca10an/a (post-training quantization, no calibration data)2026-05-27T13:06:26ZUpstream refresh note (2026-06-06): upstream head
9f226d738dee62c60a2f43b0ab7b9a2585a132c3renamed the auto-classes toMERaLiON3ASR*(model_type: meralion3 → meralion3_asr) and updated packaging/docs only — the safetensors weights are byte-identical to the Source SHA above, so this quantized pack is unaffected and remains valid.
Toolchain:
huggingface_hub: 1.11.0mlx: 0.31.2mlx-lm: 0.31.3pipeline: pipelines/mlx_direct_quantize.pypython: 3.12.13safetensors: 0.7.0transformers: 5.5.4Released under other. Upstream license of the base model applies.
On 2026-06-06 the upstream MERaLiON team published the official licence for MERaLiON/MERaLiON-3-3B-ASR: the MERaLiON Public Licence (A*STAR, © 2026).
This artifact is a quantized Derivative Work of MERaLiON/MERaLiON-3-3B-ASR and is provided under that licence:
Only the Gemma-2-2b-it text decoder under text_decoder.* is quantized. The Whisper-style speech encoder (speech_encoder.*), audio adapter (speech_audio_adapter.*), and LayerNorm bridge (ln_speech.*) are passed through in BF16 — bit-exact to the upstream weights. ASR-quality regressions vs upstream are therefore attributable to text-decoder quantization alone.