Improve model card: Add `library_name`, update `pipeline_tag`, and correct `language` entry

README.md

@@ -1,4 +1,6 @@
 ---
+base_model:
+- Qwen/Qwen3-8B-Base
 language:
 - en
 - zh
@@ -60,10 +62,11 @@ language:
 - ur
 - uz
 - yue
-base_model:
-- Qwen/Qwen3-8B-Base
 license: apache-2.0
-pipeline_tag: translation
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- translation
 ---
 
 ## LMT
@@ -73,7 +76,7 @@ pipeline_tag: translation
 **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) |
@@ -95,7 +98,9 @@ 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,