
Quantization	6-bit, `--q-bits 6` (6.501 bits per weight as reported by the converter)
Size on disk	1.0 GB
Runtime	`mlx-lm` / mlx-swift-lm
`model_type`	`gemma3_text`

This is the MLX build to use. The 4-bit sibling is 269 MB smaller but visibly degraded — see below.

Why 6-bit and not 4-bit

Naive 4-bit post-training quantization measurably damages this model. Every regression it introduces disappears at 6 bits, with no calibration step of any kind. Both builds were run on the same 16-sentence JA↔EN set, on the same machine, with a byte-identical prompt and greedy decoding, so the difference is attributable to the quantization rather than to the harness:

Source	4-bit	6-bit
来年度の予算編成	"fiscal 2022" — hallucinated year	"next fiscal year's budget"
のぞみ1号 (JA→EN)	"the Shinkansen" — train name lost	"Nozomi 1"
"I hope this message finds you well"	「今週のご挨拶」— nonsense	「…心よりお祈り申し上げます」
"Nozomi No. 1" (EN→JA)	「ノズミ1号」— misspelt	「ノゾミ1号」

Small models are disproportionately sensitive to naive PTQ. Calibrated quantization (mlx_lm.dwq, mlx_lm.awq) might reach this quality at 4-bit size, but was not needed to fix the regressions and is untried here.

Versus the GGUF

At equal size, this build is competitive with penta2himajin/MiLMMT-46-1B-v0.1-Q4_K_M-GGUF on quality and throughput, and loses on cold load — MLX has no mmap equivalent, so it pays for the whole file on every load. Timings are from a battery-powered run; read them as ratios, not absolutes.

	MLX 6-bit	GGUF Q4_K_M
Disk	1.0 GB	1.01 GB
Cold load	1546 ms	366 ms
p50 latency	251 ms	248 ms
Throughput	204 chars/s	215 chars/s

On quality the two trade places sentence by sentence. One example where this build is the better of the two: "The cabinet approved the basic policy…" renders as 「内閣は…承認した」here, against the GGUF's ungrammatical 「閣議決定は…承認した」.

Pick MLX if you are already on an MLX stack; pick the GGUF if cold load matters (an app that evicts weights between uses, for instance).

Prompt format

MiLMMT ships no chat template. Do not apply one — prompt it raw, exactly as the upstream model card specifies:

text

Translate this from <source language name> to <target language name>:
<source language name>: <source sentence>
<target language name>:

Use the language names from the upstream card's supported-language list (e.g. Japanese, English). Recommended decoding: top_k=1, temperature=0.

bash

pip install mlx-lm
PROMPT=$'Translate this from Japanese to English:\nJapanese: こんにちは、今日も一日頑張りましょう。\nEnglish:'
mlx_lm.generate --model penta2himajin/MiLMMT-46-1B-v0.1-6bit \
  --prompt "$PROMPT" --max-tokens 128 --temp 0.0

Because there is no turn marker, generation can run on into a second translation block. Stop on the string Translate this from, or trim at it.

Conversion

bash

mlx_lm.convert --hf-path xiaomi-research/MiLMMT-46-1B-v0.1 \
  -q --q-bits 6 --mlx-path MiLMMT-46-1B-v0.1-6bit

Converted with mlx-lm 0.31.3.

License

Gemma Terms of Use, inherited from the base model — not Apache 2.0. MiLMMT-46-1B is derived from Gemma 3, so redistribution and use are governed by the Gemma Terms and the Gemma Prohibited Use Policy, which pass through to anyone who obtains these weights.

Citation

bibtex

@misc{shang2026scalingmodeldatamultilingual,
      title={Scaling Model and Data for Multilingual Machine Translation with Open Large Language Models},
      author={Yuzhe Shang and Pengzhi Gao and Wei Liu and Jian Luan and Jinsong Su},
      year={2026},
      eprint={2602.11961},
      archivePrefix={arXiv},
      url={https://arxiv.org/abs/2602.11961},
}