LMT-60-8B/README.md

base_model, datasets, language, license, metrics, pipeline_tag, library_name

base_model	datasets	language	license	metrics	pipeline_tag	library_name
NiuTrans/LMT-60-8B-Base	NiuTrans/LMT-60-sft-data	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 mvf mr ms my ne ps si sw ta te tg tl ug ur uz yue	apache-2.0	bleu comet	translation	transformers

LMT

• Paper: NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs
• Github: LMT

LMT-60 is a suite of Chinese-English-centric Multilingual Machine Translation (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 GRPO 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.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 Bokmål(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), Inner Mongolian(mvf), 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={NiuTrans.LMT: 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}, 
}