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>

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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:

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:

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:

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

Here is a complete Python example:

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:

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:

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.