| | --- |
| | base_model: |
| | - NiuTrans/LMT-60-4B-Base |
| | datasets: |
| | - NiuTrans/LMT-60-sft-data |
| | 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 |
| | - nb |
| | - 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 |
| | metrics: |
| | - bleu |
| | - comet |
| | pipeline_tag: translation |
| | library_name: transformers |
| | --- |
| | |
| | ## 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.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(nb), 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}, |
| | } |
| | ``` |
