Model Sources

Paper: LLaMAX2: Your Translation-Enhanced Model Also Performs Well in Reasoning
Link: https://arxiv.org/pdf/2510.09189
Repository: https://github.com/CONE-MT/LLaMAX2.0

Model Description

Qwen3-XPlus series models start from Qwen3 instruct models with layer-slective tuning using small amount of parallel data alone.

Meanwhile, comprehensive testing on 16 reasoning tasks, such as bbeh, Livecodebench, Olymmath and so on, shows that it surpasses existing translation-enhanced models and performs on par with Qwen3 instruct models.

🔥 Excellent Translation Performance

Qwen3-XPlus significantly boost translation performance in both high- and low-resource languages.

🔥 Excellent Reasoning Performance

Trained Data Covered Languages

en (English)
ar (Arabic)
bn (Bengali)
cs (Czech)
de (German)
es (Spanish)
fr (French)
hu (Hungarian)
ja (Japanese)
ko (Korean)
ru (Russian)
sr (Serbian)
sw (Swahili)
te (Telugu)
th (Thai)
vi (Vietnamese)
zh (Chinese)

Model Index

We implement multiple versions of the Qwen3-XPlus model, the model links are as follows:

Model	LLaMAX
Qwen3-XPlus-17langs-8B	Link
👉 Qwen3-XPlus-17langs-14B	Link

Citation

If our model helps your work, please cite this paper:

@misc{gaoLLaMAX2YourTranslationEnhanced2025,
  title = {{{LLaMAX2}}: {{Your Translation-Enhanced Model}} Also {{Performs Well}} in {{Reasoning}}},
  shorttitle = {{{LLaMAX2}}},
  author = {Gao, Changjiang and Huang, Zixian and Gong, Jingyang and Huang, Shujian and Li, Lei and Yuan, Fei},
  year = {2025},
  month = oct,
  number = {arXiv:2510.09189},
  eprint = {2510.09189},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.2510.09189},
  archiveprefix = {arXiv}
}