| --- |
| base_model: |
| - Qwen/Qwen3-8B-Base |
| language: |
| - en |
| - zh |
| - ar |
| - es |
| - de |
| - fr |
| - it |
| - ja |
| - nl |
| - pl |
| - pt |
| - ru |
| - tr |
| - bg |
| - bn |
| - cs |
| - da |
| - el |
| - fa |
| - fi |
| - hi |
| - hu |
| - id |
| - ko |
| - no |
| - ro |
| - sk |
| - sv |
| - th |
| - uk |
| - vi |
| - am |
| - az |
| - bo |
| - he |
| - hr |
| - hy |
| - is |
| - jv |
| - ka |
| - kk |
| - km |
| - ky |
| - lo |
| - mn |
| - mr |
| - ms |
| - my |
| - ne |
| - ps |
| - si |
| - sw |
| - ta |
| - te |
| - tg |
| - tl |
| - ug |
| - ur |
| - uz |
| - yue |
| license: apache-2.0 |
| pipeline_tag: text-generation |
| library_name: transformers |
| tags: |
| - translation |
| --- |
| |
| ## LMT |
| - Paper: [Beyond English: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs](https://arxiv.org/abs/2511.07003) |
| - Github: [LMT](https://github.com/NiuTrans/LMT) |
|
|
| **LMT-60** is a suite of **Chinese-English-centric** MMT models trained on **90B tokens** mixed monolingual and bilingual tokens, covering **60 languages across 234 translation directions** and achieving **SOTA performance** among models with similar language coverage. |
| We release both the CPT and SFT versions of LMT-60 in four sizes (0.6B/1.7B/4B/8B). All checkpoints are available: |
| | Models | Model Link | |
| |:------------|:------------|\ |
| | LMT-60-0.6B-Base | [NiuTrans/LMT-60-0.6B-Base](https://huggingface.co/NiuTrans/LMT-60-0.6B-Base) | |
| | LMT-60-0.6B | [NiuTrans/LMT-60-0.6B](https://huggingface.co/NiuTrans/LMT-60-0.6B) | |
| | LMT-60-1.7B-Base | [NiuTrans/LMT-60-1.7B-Base](https://huggingface.co/NiuTrans/LMT-60-1.7B-Base) | |
| | LMT-60-1.7B | [NiuTrans/LMT-60-1.7B](https://huggingface.co/NiuTrans/LMT-60-1.7B) | |
| | LMT-60-4B-Base | [NiuTrans/LMT-60-4B-Base](https://huggingface.co/NiuTrans/LMT-60-4B-Base) | |
| | LMT-60-4B | [NiuTrans/LMT-60-4B](https://huggingface.co/NiuTrans/LMT-60-4B) | |
| | LMT-60-8B-Base | [NiuTrans/LMT-60-8B-Base](https://huggingface.co/NiuTrans/LMT-60-8B-Base) | |
| | LMT-60-8B | [NiuTrans/LMT-60-8B](https://huggingface.co/NiuTrans/LMT-60-8B) | |
|
|
| Our supervised fine-tuning (SFT) data are released at [NiuTrans/LMT-60-sft-data](https://huggingface.co/datasets/NiuTrans/LMT-60-sft-data) |
|
|
| ## Quickstart |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| |
| model_name = "NiuTrans/LMT-60-8B" |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side='left') |
| model = AutoModelForCausalLM.from_pretrained(model_name) |
| |
| prompt = "Translate the following text from English into Chinese. |
| English: The concept came from China where plum blossoms were the flower of choice. |
| Chinese: " |
| messages = [{"role": "user", "content": prompt}] |
| text = tokenizer.apply_chat_template( |
| messages, |
| tokenize=False, |
| add_generation_prompt=True, |
| ) |
| model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
| |
| generated_ids = model.generate(**model_inputs, max_new_tokens=512, num_beams=5, do_sample=False) |
| output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() |
| |
| outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True) |
| |
| print("response:", outputs) |
| ``` |
|
|
| ## Support Languages |
|
|
| | Resource Tier | Languages | |
| | :---- | :---- | |
| | High-resource Languages (13) | Arabic(ar), English(en), Spanish(es), German(de), French(fr), Italian(it), Japanese(ja), Dutch(nl), Polish(pl), Portuguese(pt), Russian(ru), Turkish(tr), Chinese(zh) | |
| | Medium-resource Languages (18) | Bulgarian(bg), Bengali(bn), Czech(cs), Danish(da), Modern Greek(el), Persian(fa), Finnish(fi), Hindi(hi), Hungarian(hu), Indonesian(id), Korean(ko), Norwegian(no), Romanian(ro), Slovak(sk), Swedish(sv), Thai(th), Ukrainian(uk), Vietnamese(vi) | |
| | Low-resouce Languages (29) | Amharic(am), Azerbaijani(az), Tibetan(bo), Modern Hebrew(he), Croatian(hr), Armenian(hy), Icelandic(is), Javanese(jv), Georgian(ka), Kazakh(kk), Central Khmer(km), Kirghiz(ky), Lao(lo), Chinese Mongolian(mn_cn), Marathi(mr), Malay(ms), Burmese(my), Nepali(ne), Pashto(ps), Sinhala(si), Swahili(sw), Tamil(ta), Telugu(te), Tajik(tg), Tagalog(tl), Uighur(ug), Urdu(ur), Uzbek(uz), Yue Chinese(yue) | |
| |
| ## Citation |
| |
| If you find our paper useful for your research, please kindly cite our paper: |
| ```bash |
| @misc{luoyf2025lmt, |
| title={Beyond English: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs}, |
| author={Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang, Murun Yang, Dingyang Lin, Kaiyan Chang, Tong Zheng, Bei Li, Peinan Feng, Quan Du, Tong Xiao, Jingbo Zhu}, |
| year={2025}, |
| eprint={2511.07003}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2511.07003}, |
| } |
| ``` |
