metadata
base_model:
- Qwen/Qwen3-4B-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
LMT
- Paper: Beyond English: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs
- Github: 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 |
| LMT-60-0.6B | NiuTrans/LMT-60-0.6B |
| LMT-60-1.7B-Base | NiuTrans/LMT-60-1.7B-Base |
| LMT-60-1.7B | NiuTrans/LMT-60-1.7B |
| LMT-60-4B-Base | NiuTrans/LMT-60-4B-Base |
| LMT-60-4B | NiuTrans/LMT-60-4B |
| LMT-60-8B-Base | NiuTrans/LMT-60-8B-Base |
| LMT-60-8B | NiuTrans/LMT-60-8B |
Our supervised fine-tuning (SFT) data are released at NiuTrans/LMT-60-sft-data
Quickstart
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:
@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},
}