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
Table with columns: Baseline, Win, Lose, Gap| Baseline | Win | Lose | Gap |
|---|
| Qwen3.5-9B | 83.2 | 13.7 | +69.5 |
| HY-MT2-7B | 52.4 | 40.8 | +11.6 |
| Gemma4-12B | 52.3 | 38.3 | +14.0 |
| DeepSeek-v4-flash | 38.7 | 52.4 | -13.7 |
IFMTBench (multi-constraint)
Table with columns: Baseline, Win, Lose, Gap| Baseline | Win | Lose | Gap |
|---|
| Qwen3.5-9B | 52.5 | 26.6 | +25.9 |
| HY-MT2-7B | 45.9 | 28.5 | +17.4 |
| Gemma4-12B | 31.9 | 30.8 | +1.1 |
| DeepSeek-v4-flash | 28.3 | 38.8 | -10.5 |
IF-WMT25 (by direction)
Table with columns: Direction, Qwen3.5-9B W/L/G, HY-MT2-7B W/L/G, Gemma4-12B W/L/G, DeepSeek-v4-flash W/L/G| Direction | Qwen3.5-9B W/L/G | HY-MT2-7B W/L/G | Gemma4-12B W/L/G | DeepSeek-v4-flash W/L/G |
|---|
| cs→de | 81.1 / 18.9 / +62.2 | 45.2 / 52.4 / -7.2 | 44.8 / 48.0 / -3.2 | 29.9 / 63.8 / -33.9 |
| cs→uk | 84.1 / 15.1 / +69.0 | 43.9 / 52.0 / -8.1 | 53.4 / 42.5 / +10.9 | 29.9 / 63.8 / -33.9 |
| cs→vi | 80.3 / 17.3 / +63.0 | 61.6 / 36.8 / +24.8 | 46.6 / 40.6 / +6.0 |
IFMTBench multi-constraint (by direction)
Table with columns: Direction, Qwen3.5-9B W/L/G, HY-MT2-7B W/L/G, Gemma4-12B W/L/G, DeepSeek-v4-flash W/L/G| Direction | Qwen3.5-9B W/L/G | HY-MT2-7B W/L/G | Gemma4-12B W/L/G | DeepSeek-v4-flash W/L/G |
|---|
| de→en | 75.0 / 0.0 / +75.0 | 37.5 / 37.5 / 0.0 | 12.5 / 37.5 / -25.0 | 25.0 / 25.0 / 0.0 |
| en→de | 63.6 / 27.3 / +36.3 | 54.5 / 45.5 / +9.0 | 18.2 / 72.7 / -54.5 | 18.2 / 63.6 / -45.4 |
| en→ja | 64.3 / 21.4 / +42.9 | 40.0 / 33.3 / +6.7 | 53.3 / 26.7 / +26.6 |
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}
Requires a recent transformers with Qwen3.5 support (transformers>=5.2.0 recommended).
import torchfrom 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
Table with columns: Languages, Abbr., Chinese Names| Languages | Abbr. | Chinese Names |
|---|
| English | en | 英语 |
| Simplified Chinese | zh | 简体中文 |
| Traditional Chinese | zh-Hant | 繁体中文 |
| Czech | cs | 捷克语 |
| Japanese | ja | 日语 |
| Korean | ko | 韩语 |
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}}
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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