Part of the Hy-MT2 MLX collection.

Hy-MT2-1.8B-4bit (MLX)

Apple MLX weights for Tencent-Hunyuan/Hy-MT2-1.8B, Tencent Hunyuan's multilingual translation model.

TL;DR


Variant	1.8B 4-bit
Best for	smallest 1.8B checkpoint and Space load smoke test
Runtime	`mlx-lm`
Official code	`Tencent-Hunyuan/Hy-MT2`
MLX code	`ailuntx/Hy-MT2-MLX`
Hardware	Apple Silicon recommended; HF Spaces CPU fallback is only a load smoke test

Quick Start

pip install mlx-lm
PROMPT=$'Translate the following text into English. Note that you should only output the translated result without any additional explanation:\n\n今天天气真好。'
mlx_lm.generate --model mlx-community/Hy-MT2-1.8B-4bit \
  --prompt "$PROMPT" \
  --max-tokens 128 \
  --temp 0.7 \
  --top-p 0.6 \
  --top-k 20

For conversion and staging tools:

git clone https://github.com/ailuntx/Hy-MT2-MLX.git
cd Hy-MT2-MLX
python scripts/infer_mlx.py --model /path/to/mlx/checkpoint --text "今天天气真好。" --target-lang English

Variants

Variant	Best for
`Hy-MT2-1.8B-bfloat16`	high-quality 1.8B baseline
`Hy-MT2-1.8B-8bit`	smaller 1.8B checkpoint
`Hy-MT2-1.8B-4bit`	smallest 1.8B checkpoint
`Hy-MT2-7B-bfloat16`	high-precision 7B conversion
`Hy-MT2-7B-8bit`	7B size/quality middle ground
`Hy-MT2-7B-4bit`	smallest 7B checkpoint

Conversion Notes

Component	Source	MLX handling
model weights	official dense Hy-MT2 checkpoint	converted with MLX/`mlx-lm` tooling
tokenizer/config	official checkpoint	copied through for `mlx-lm` loading
quantized variants	bfloat16 MLX baseline	derived with MLX quantization settings

Validation

Local Apple Silicon is the intended runtime. The Hy-MT2 HF Space starts and loads the model on Linux CPU fallback, but cpu-basic can exceed request timeouts even for very small generation tests.

License

License follows the upstream Tencent Hunyuan release.

Citation

@misc{hymt2-mlx,
  title  = {Hy-MT2-MLX: Apple MLX port of Hy-MT2},
  author = {ailuntx},
  year   = {2026},
  url    = {https://github.com/ailuntx/Hy-MT2-MLX},
}

@misc{zheng2026hymt2familyfastefficient,
  title         = {Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild},
  author        = {Mao Zheng and Zheng Li and Tao Chen and Bo Lv and Mingrui Sun and Mingyang Song and Jinlong Song and Hong Huang and Decheng Wu and Hai Wang and Yifan Song and Yanfeng Chen and Guanwei Zhang},
  year          = {2026},
  eprint        = {2605.22064},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2605.22064},
}

Loading…

Variant

1.8B 4-bit

Best for

smallest 1.8B checkpoint and Space load smoke test

Runtime

mlx-lm

Official code

Tencent-Hunyuan/Hy-MT2

MLX code

ailuntx/Hy-MT2-MLX

Hardware

Apple Silicon recommended; HF Spaces CPU fallback is only a load smoke test

Quick Start

pip install mlx-lm PROMPT=$'Translate the following text into English. Note that you should only output the translated result without any additional explanation:\n\n今天天气真好。' mlx_lm.generate --model mlx-community/Hy-MT2-1.8B-4bit \ --prompt "$PROMPT" \ --max-tokens 128 \ --temp 0.7 \ --top-p 0.6 \ --top-k 20

For conversion and staging tools:

git clone https://github.com/ailuntx/Hy-MT2-MLX.git cd Hy-MT2-MLX python scripts/infer_mlx.py --model /path/to/mlx/checkpoint --text "今天天气真好。" --target-lang English

Variant

Best for

Hy-MT2-1.8B-bfloat16

high-quality 1.8B baseline

Hy-MT2-1.8B-8bit

smaller 1.8B checkpoint

Hy-MT2-1.8B-4bit

smallest 1.8B checkpoint

Hy-MT2-7B-bfloat16

high-precision 7B conversion

Hy-MT2-7B-8bit

7B size/quality middle ground

Hy-MT2-7B-4bit

smallest 7B checkpoint

Component

Source

MLX handling

model weights

official dense Hy-MT2 checkpoint

converted with MLX/mlx-lm tooling

tokenizer/config

official checkpoint

copied through for mlx-lm loading

quantized variants

bfloat16 MLX baseline

derived with MLX quantization settings

Citation

@misc{hymt2-mlx, title = {Hy-MT2-MLX: Apple MLX port of Hy-MT2}, author = {ailuntx}, year = {2026}, url = {https://github.com/ailuntx/Hy-MT2-MLX}, } @misc{zheng2026hymt2familyfastefficient, title = {Hy-MT2: A Family of Fast, Efficient and Powerful Multilingual Translation Models in the Wild}, author = {Mao Zheng and Zheng Li and Tao Chen and Bo Lv and Mingrui Sun and Mingyang Song and Jinlong Song and Hong Huang and Decheng Wu and Hai Wang and Yifan Song and Yanfeng Chen and Guanwei Zhang}, year = {2026}, eprint = {2605.22064}, archivePrefix = {arXiv}, primaryClass = {cs.CL}, url = {https://arxiv.org/abs/2605.22064}, }