Qingqiu-MT-9B

Model Introduction

Qingqiu-MT-9B is an instruction-following multilingual translation model from Kingsoft Office WPS-Qingqiu Team, released for the constrained track of the WMT26 General Machine Translation Shared Task.

It is based on Qwen/Qwen3.5-9B and primarily supports mutual translation among 20 languages, covering 22 of the 23 official WMT26 language pairs (excluding English→Northern Sámi). Unlike conventional MT settings that only provide source text and a target language, each sample also includes a natural-language translation instruction. The model is expected to follow that instruction and output only the translation.

License: Apache 2.0. For system details, see our WMT26 system paper (Qingqiu-MT-9B: An Instruction-Following Multilingual Translation Model).

System Overview

Evaluation Results

We report development results on IF-WMT25 (main set; 5,239 samples covering 22 WMT26 directions) and IFMTBench multi-constraint subset (2,838 samples). Evaluation uses an LLM-as-a-judge protocol (GLM-5): for each source text and instruction, anonymous system outputs are scored jointly on Accuracy / Terminology / Format / Fluency / Style (0–100), and the overall score is a weighted sum with weights (0.40, 0.20, 0.15, 0.15, 0.10). Tables report pairwise Win / Lose / Gap (%) of Qingqiu-MT-9B vs each baseline (Gap = Win − Lose; ties counted in the denominator). Overall is sample-level aggregation over the full set.

Baselines: Qwen3.5-9B, HY-MT2-7B, Gemma4-12B, DeepSeek-v4-flash.

Overall summary

IF-WMT25

IFMTBench (multi-constraint)

IF-WMT25 (by direction)

IFMTBench multi-constraint (by direction)

Prompts

Use the English name from the language table below for {src_lang} / {tgt_lang} (not the abbreviation). Separate paragraphs in {text} with blank lines (\n\n).

Translate the following {src_lang} text into {tgt_lang}. Strictly follow the instruction below. Output only the translation.

instruction:
{instruction}

text:
{text}

Use with transformers

Requires a recent transformers with Qwen3.5 support (transformers>=5.2.0 recommended).

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name_or_path = "WPS-Qingqiu/Qingqiu-MT-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name_or_path,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

PROMPT = (
    "Translate the following {src_lang} text into {tgt_lang}. "
    "Strictly follow the instruction below. Output only the translation.\n\n"
    "instruction:\n{instruction}\n\n"
    "text:\n{text}"
)

messages = [
    {
        "role": "user",
        "content": PROMPT.format(
            src_lang="English",
            tgt_lang="Simplified Chinese",
            instruction="Use a formal register suitable for an official notice. Output only the translation.",
            text="Campus dining hall hours will change next week.\n\nBreakfast starts at 7:00.",
        ),
    }
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,  # non-thinking mode
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=2048,
    do_sample=True,
    temperature=0.3,
    top_p=0.90,
    top_k=20,
    repetition_penalty=1.05,
)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True).strip())

We recommend using the following set of parameters for inference:

{
  "top_k": 20,
  "top_p": 0.90,
  "repetition_penalty": 1.05,
  "temperature": 0.3
}

Supported languages

Main WMT26 directions:
cs→de/uk/vi, en→arz/be/cs/de/et/hy/id/is/ja/kk/ko/lij/lld/ru/th/uk/zh/zh-Hant, zh→ja.

Citation

@inproceedings{xia2026qingqiu,
  title={Qingqiu-MT-9B: An Instruction-Following Multilingual Translation Model},
  author={Xia, Tian and Chen, Chao and Yang, Mengpeng and Yang, Jingxu and Sun, Yabo and Liu, Qiang},
  booktitle={Proceedings of the Eleventh Conference on Machine Translation (WMT)},
  year={2026}
}

Authors & contact

Tian Xia, Chao Chen, Mengpeng Yang, Jingxu Yang, Yabo Sun, Qiang Liu
Kingsoft Office WPS-Qingqiu / Wuhan, China

xiatian3@wps.cn, chenchao9@wps.cn, yangmengpeng@wps.cn, yangjingxu1@wps.cn, sunyabo@wps.cn, liuqiang2@wps.cn

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