---
title: Marco-MT-Algharb
canonical_url: "https://www.modelscope.cn/models/ATH-MaaS/Marco-MT-Algharb"
md_url: "https://www.modelscope.cn/models/ATH-MaaS/Marco-MT-Algharb.md"
repository: ATH-MaaS/Marco-MT-Algharb
last_updated: 2026-07-14
license: apache-2.0
pipeline_tag: translation
tasks:
  - translation
model_type:
  - qwen3
architectures:
  - Qwen3ForCausalLM
parameters: 14.8B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
  - zh
  - ru
  - uk
  - cs
  - ja
  - ko
  - et
  - sr
  - ar
  - de
  - bho
downloads: 365
stars: 4
---

# Marco-MT-Algharb

> Marco-MT-Algharb - ATH-MaaS 在 ModelScope 开源的模型。Alibaba International Digital Commerce

ATH-MaaS/Marco-MT-Algharb 是 ModelScope 魔搭社区上的 14.8B 参数translation模型，采用 apache-2.0 许可。

- **Repository**: ATH-MaaS/Marco-MT-Algharb
- **License**: apache-2.0
- **Tasks**: translation
- **Parameters**: 14.8B
- **Downloads**: 365
- **Stars**: 4
- **Last updated**: 2026-07-14

Source: https://www.modelscope.cn/models/ATH-MaaS/Marco-MT-Algharb

---

<div align="center">

  <a href="https://www.aidc-ai.com/marcomt">
    <img src="./marco_mt_label.png" width="400">
  </a>

  <a href="https://www.aidc-ai.com/marcomt">Alibaba International Digital Commerce</a>
  <br>

  <a href="https://github.com/AIDC-AI/Marco-MT"><img src="https://img.shields.io/badge/GitHub-Repository-181717?logo=github" alt="GitHub"></a><a href="https://huggingface.co/AIDC-AI/Marco-MT-Algharb"><img src="https://img.shields.io/badge/Hugging%20Face-Model-ffc107?logo=huggingface" alt="Hugging Face"></a><a href="https://www2.statmt.org/wmt25/pdf/2025.wmt-1.33.pdf"><img src="https://img.shields.io/badge/Paper-WMT_2025-B23737" alt="Paper"></a><a href="https://huggingface.co/spaces/AIDC-AI/Marco-MT-Algharb"><img src="https://img.shields.io/badge/Demo-HF%20Space-ffc107?logo=huggingface" alt="Demo"></a>

</div>


# Marco-MT-Algharb

This repository contains the system for Algharb, the submission from the Marco Translation Team of Alibaba International Digital Commerce (AIDC) to the WMT 2025 General Machine Translation Shared Task.

## Introduction

The Algharb system is a large translation model built based on the Qwen3-14B foundation. It is designed for high-quality translation across 13 diverse language directions and demonstrates state-of-the-art performance. Our approach is centered on a multi-stage refinement pipeline that systematically enhances translation fluency and faithfulness.

Supported language pairs:
| Languages pair | Chinese Names |
|---|---|
| en2zh |  英语到中文 |
| en2ja |  英语到日语 |
| en2ko |  英语到韩语 |
| en2ar |  英语到阿拉伯语 |
| en2et |  英语到爱沙尼亚语 |
| en2sr_latin |  英语到塞尔维亚语(拉丁化) |
| en2ru |  英语到俄语 |
| en2uk |  英语到乌克兰语 |
| en2cs |  英语到捷克语 |
| en2bho |  英语到博杰普尔语 |
| cs2uk |  捷克语到乌克兰语 |
| cs2de |  捷克语到德语 |
| ja2zh |  日语到中文 |

## Usage

The model expects a specific instruction format for translation. The following example demonstrates how to construct the prompt and perform generation using the vllm library for efficient inference.


### 1. Dependencies

First, ensure you have the necessary libraries installed:

```bash
pip install torch transformers==4.55.0 vllm==0.10.0 unbabel-comet==2.2.2
```

### 2. Prompt Format and Decoding

The core of the process involves formatting the input text into a specific prompt template and then using the vllm engine to generate translations. For our hybrid decoding strategy, we generate multiple candidates (n > 1) for later re-ranking.
The prompt template is:

```python
"Human: Please translate the following text into {target_language}: \n{source_text}<|im_end|>\nAssistant:"
```

Here is a complete Python example:
```python
from vllm import LLM, SamplingParams

model_path = "path/to/your/algharb_model"
llm = LLM(model=model_path)

source_text = "This paper presents the Algharb system, our submission to the WMT 2025."
source_lang_code = "en" 
target_lang_code = "zh"

lang_name_map = {
    "en": "english"
    "zh": "chinese",
    "ko": "korean",
    "ja": "japanese",
    "ar": "arabic",
    "cs": "czech",
    "ru": "russian",
    "uk": "ukraine",
    "et": "estonian",
    "bho": "bhojpuri",
    "sr_latin": "serbian",
    "de": "german",
}

target_language_name = lang_name_map.get(target_lang_code, "the target language")

prompt = (
    f"Human: Please translate the following text into {target_language_name}: \n"
    f"{source_text}<|im_end|>\n"
    f"Assistant:"
)

prompts_to_generate = [prompt]
print("Formatted Prompt:\n", prompt)

sampling_params = SamplingParams(
    n=1,
    temperature=0.001,
    top_p=0.001,
    max_tokens=512
)

outputs = llm.generate(prompts_to_generate, sampling_params)

for output in outputs:
    generated_text = output.outputs[0].strip()
    print(f"translation: {generated_text}")
```

## Apply MBR decoding
First, run random sample decoding:
```python
from vllm import LLM, SamplingParams

model_path = "path/to/your/algharb_model"
llm = LLM(model=model_path)

source_text = "This paper presents the Algharb system, our submission to the WMT 2025."
source_lang_code = "en" 
target_lang_code = "zh"

lang_name_map = {
    "en": "english"
    "zh": "chinese",
    "ko": "korean",
    "ja": "japanese",
    "ar": "arabic",
    "cs": "czech",
    "ru": "russian",
    "uk": "ukraine",
    "et": "estonian",
    "bho": "bhojpuri",
    "sr_latin": "serbian",
    "de": "german",
}

target_language_name = lang_name_map.get(target_lang_code, "the target language")


prompt = (
    f"Human: Please translate the following text into {target_language_name}: \n"
    f"{source_text}<|im_end|>\n"
    f"Assistant:"
)

prompts_to_generate = [prompt]
print("Formatted Prompt:\n", prompt)

sampling_params = SamplingParams(
    n=100,
    temperature=1,
    top_p=1,
    max_tokens=512
)

outputs = llm.generate(prompts_to_generate, sampling_params)

# The 'outputs' list contains one item for each prompt.
for output in outputs:
    prompt_used = output.prompt
    print(f"\n--- Candidates for source: '{source_text}' ---")
    
    # Each output object contains 'n' generated sequences.
    for i, candidate in enumerate(output.outputs):
        generated_text = candidate.text.strip()
        print(f"Candidate {i+1}: {generated_text}")
```
Then, run MBR decoding:
```bash
comet-mbr -s src.txt -t mbr_sample_100.txt -o mbr_trans.txt --num_samples 100 --gpus 1 --qe_model Unbabel/wmt22-cometkiwi-da
```
Note: Word alignment for MBR reranking will be available soon. 


## License
This model is licensed under Apache License Version 2 (https://www.apache.org/licenses/LICENSE-2.0.txt, SPDX-License-identifier: Apache-2.0).

## Disclaimer
We used compliance checking algorithms during the training process, to ensure the compliance of the trained model(s) to the best of our ability. Due to complex data and the diversity of language model usage scenarios, we cannot guarantee that the model is completely free of copyright issues or improper content. If you believe anything infringes on your rights or generates improper content, please contact us, and we will promptly address the matter.