
Variant	1.8B 8-bit
Best for	smaller 1.8B checkpoint
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

bash

pip install mlx-lm
PROMPT=$'Translate the following text into English. Note that you should only output the translated result without any additional explanation:\n\n今天天气真好。'
mlx_lm.generate --model mlx-community/Hy-MT2-1.8B-8bit \
  --prompt "$PROMPT" \
  --max-tokens 128 \
  --temp 0.7 \
  --top-p 0.6 \
  --top-k 20

For conversion and staging tools:

bash

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

bibtex

@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},
}