Update metadata (library_name, pipeline_tag, language) and add paper abstract

This PR significantly improves the model card for **LMT** by:

1. **Adding `library_name: transformers`** to the metadata. This is evidenced by the `transformers` library usage in the Quickstart section and `config.json`, enabling the automated "how to use" code snippet on the Hugging Face Hub.
2. **Changing `pipeline_tag: translation` to `pipeline_tag: text-generation`** in the metadata, aligning with the guidelines for LLMs performing generation tasks, including translation.
3. **Correcting the `language` metadata** by removing the erroneous `false` entry and adding `no` (Norwegian), which was listed in the "Support Languages" table but missing from the metadata. This ensures an accurate representation of the model's language coverage.
4. **Adding the paper's abstract** to the model card content. This provides a concise summary of the model, its innovations, and key findings, improving the overall context and clarity for users visiting the model page.

The existing paper link, GitHub repository link, and sample usage section are accurate and have been retained without modification.

README.md

@@ -1,4 +1,6 @@
 ---
+base_model:
+- Qwen/Qwen3-0.6B-Base
 language:
 - en
 - zh
@@ -60,10 +62,9 @@ language:
 - ur
 - uz
 - yue
-base_model:
-- Qwen/Qwen3-0.6B-Base
 license: apache-2.0
-pipeline_tag: translation
+pipeline_tag: text-generation
+library_name: transformers
 ---
 
 ## LMT
@@ -71,7 +72,12 @@ pipeline_tag: translation
 - 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:
+We release both the CPT and SFT versions of LMT-60 in four sizes (0.6B/1.7B/4B/8B).
+
+## Abstract
+Large language models have significantly advanced Multilingual Machine Translation (MMT), yet the broad language coverage, consistent translation quality, and English-centric bias remain open challenges. To address these challenges, we introduce **LMT**, a suite of **L**arge-scale **M**ultilingual **T**ranslation models centered on both Chinese and English, covering 60 languages and 234 translation directions. During development, we identify a previously overlooked phenomenon of **directional degeneration**, where symmetric multi-way fine-tuning data overemphasize reverse directions (X $\to$ En/Zh), leading to excessive many-to-one mappings and degraded translation quality. We propose **Strategic Downsampling**, a simple yet effective method to mitigate this degeneration. In addition, we design **Parallel Multilingual Prompting (PMP)**, which leverages typologically related auxiliary languages to enhance cross-lingual transfer. Through rigorous data curation and refined adaptation strategies, LMT achieves SOTA performance among models of comparable language coverage, with our 4B model (LMT-60-4B) surpassing the much larger Aya-101-13B and NLLB-54B models by a substantial margin. We release LMT in four sizes (0.6B/1.7B/4B/8B) to catalyze future research and provide strong baselines for inclusive, scalable, and high-quality MMT.
+
+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) |
@@ -95,7 +101,9 @@ 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.\nEnglish: The concept came from China where plum blossoms were the flower of choice.\nChinese: "
+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,
@@ -105,7 +113,7 @@ text = tokenizer.apply_chat_template(
 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() 
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
 
 outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
 
@@ -125,12 +133,12 @@ print("response:", outputs)
 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}, 
+      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}, 
+      url={https://arxiv.org/abs/2511.07003},
 }
 ```