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Hunyuan-MT 1.5: How a Tiny 1.8B Model Beats Commercial Translation APIs

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Hunyuan-MT 1.5: How a 1.8B Model Delivers Champion-Level Translation

In the world of machine translation, a persistent dilemma exists: should we chase the highest possible translation quality, or prioritize deployment efficiency and inference speed? Traditionally, larger models with more parameters promised better results, but at the cost of significant computational expense and high deployment barriers. Tencent Hunyuan’s newly open-sourced HY-MT1.5 series directly tackles this challenge. It consists of two members: a nimble 1.8B “lightweight contender” and a powerful 7B “champion heavyweight.” Remarkably, the 1.8B model—with less than one-third the parameters of its larger sibling—achieves translation quality that is “close” to the 7B version. How is this possible? And for developers, researchers, and enterprises, how should you choose and use these models? This article provides a comprehensive guide.

The Core Innovation: How Does 1.8B Rival 7B?

What is the Hunyuan Translation Model HY-MT1.5?

The Hunyuan Translation Model Version 1.5 (HY-MT1.5) comprises two high-performance neural machine translation models released by Tencent. They are not mere iterations but are precisely engineered for different application scenarios:

  • HY-MT1.5-7B: This is a “large model” with 7 billion parameters. It is an upgraded version of the team’s WMT25 champion model, specifically optimized for handling explanatory translation and mixed-language scenarios. In simpler terms, it doesn’t just translate accurately; it produces more natural and fluent results that better align with target language conventions, especially when dealing with complex sentences or culturally specific expressions.
  • HY-MT1.5-1.8B: This is a “small model” with only 1.8 billion parameters. Its standout feature is achieving translation quality “close” to the 7B version despite a drastically smaller parameter count (less than one-third). This enables a true “fast and accurate” balance.

Core Features and Advantages: Let the Data Speak

Why are these models noteworthy? Let’s look at their concrete, quantifiable strengths:

  1. The 1.8B Model: A Performance Leader in Its Class

    • Performance Quantified: According to the official comprehensive performance chart, the HY-MT1.5-1.8B achieves state-of-the-art results among models of its parameter scale (~1.8B). The report states its performance exceeds that of most commercial translation APIs. This makes it an extremely attractive option for developers seeking a low-cost, high-quality translation solution.
    • Deployment Advantage Quantified: Its 1.8B size is its core strength. After quantization (such as the INT4 or FP8 quantization discussed later), the model can be easily deployed on edge devices (like mobile phones, edge computing boxes) and meet the stringent low-latency demands of real-time translation scenarios (e.g., live speech transcription, instant webpage translation). This is the source of its wide applicability.

  2. The 7B Model: A Comprehensive Evolution of the Champion

    • Compared to the version open-sourced in September 2025, the HY-MT1.5-7B primarily enhances the ability to handle complex content, particularly improving the coherence and accuracy when translating documents containing annotations or mixed-language text.

  3. Shared Advanced Features

    • Both models support three advanced features critical for professional translation:
      • Terminology Intervention: Ensures consistent translation of domain-specific terms (e.g., company names, product names, technical jargon).
      • Context-Aware Translation: Translates by incorporating context from preceding sentences to avoid referential ambiguity.
      • Format-Preserving Translation: Translates plain text while attempting to preserve original formatting markers (e.g., HTML tags, section numbers), which is crucial for technical documentation and manuals.

Performance in Practice: A Clear Visual Comparison

A model’s worth is ultimately judged by its results. The overall performance chart from the official technical report provides a clear visual comparison. It shows that while the HY-MT1.5-1.8B has fewer parameters, its scores on multiple language pair translation quality evaluations closely trail the HY-MT1.5-7B and significantly lead other comparable models. The HY-MT1.5-7B, meanwhile, ranks at the top in numerous evaluations, demonstrating the formidable strength of its “champion-upgraded” pedigree.

For more detailed experimental data, ablation studies, and the underlying technical principles, you can refer directly to the official Technical Report.

How to Get Started: A Complete Guide from Inference to Deployment

Convinced of the models’ capabilities? The next step is hands-on practice. The Hunyuan translation models offer multiple usage paths, from quick testing with a few lines of code to production-grade, high-concurrency deployment.

Step 1: Basic Inference (Using the Transformers Library)

This is the fastest way to experiment. First, ensure you have the correct library version installed:

pip install transformers==4.56.0

Then, you can use the following Python code to load a model and perform translation:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "tencent/HY-MT1.5-7B" # Can also be "tencent/HY-MT1.5-1.8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# Build a translation request: English to Chinese
messages = [
    {"role": "user", "content": "Translate the following segment into Chinese, without additional explanation.\n\nGet something off your chest"},
]
# Format the input using the chat template
tokenized_input = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=False,
    return_tensors="pt"
).to(model.device)

# Generate the translation
outputs = model.generate(tokenized_input, max_new_tokens=2048)
translated_text = tokenizer.decode(outputs[0])
print(translated_text)

Recommended Inference Parameters for Best Results:
Based on official experience, using the following set of parameters typically yields more stable and higher-quality translation output:

{
  "top_k": 20,
  "top_p": 0.6,
  "repetition_penalty": 1.05,
  "temperature": 0.7
}

Step 2: Mastering Advanced Prompt Techniques

The Hunyuan translation models activate advanced features through specific instruction templates. Using these templates correctly is key to unlocking their full potential.

Basic Translation (e.g., EN -> ZH)

Translate the following text into {target_language}. Output only the translation without additional explanation.

{source_text}

Terminology Intervention

When you need to ensure “iPhone” is always translated as “苹果手机”:

Use the following reference for translation:
iPhone should be translated as 苹果手机

Translate the following text into Chinese. Output only the translation without additional explanation:
The new iPhone features are impressive.

Context-Aware Translation

To translate a sentence that references prior context more accurately:

Previous Context: The project manager mentioned that the "Apollo" project will launch next week.
Using the information above, translate the following text into English. Do not translate the previous context and do not provide extra explanation:
请确保所有团队成员都清楚Apollo的里程碑。

Format-Preserving Translation

To preserve sequence tags like <sn>1.2</sn> when translating technical documents:

Translate the text between <source></source> into Chinese. Output only the translation without additional explanation. The <sn></sn> tags in the original text indicate formatting information; try to retain these tags at corresponding positions in the translation. Output format: <target>str</target>

<source>Follow the steps: <sn>1</sn> Power on. <sn>2</sn> Connect to Wi-Fi.</source>

Step 3: Choosing a Production Deployment Solution

When integrating the model into a product to serve many users, you need a professional inference deployment framework. Here is a comparison of three mainstream options:

Deployment Framework Core Advantage Ideal Use Case
TensorRT-LLM NVIDIA-optimized,极致 inference performance, lowest latency. Production systems with extremely high latency requirements running on NVIDIA GPU environments.
vLLM High throughput, optimized attention algorithms, active open-source community. Online API services needing to handle massive concurrent translation requests.
sglang Specialized runtime for LLM inference, simple design. Scenarios prioritizing deployment simplicity or wanting to use an emerging, efficient runtime.

Option A: Deployment with TensorRT-LLM (For Peak Performance)

For scenarios demanding the lowest latency and highest GPU utilization, TensorRT-LLM is the top choice. Tencent provides a pre-built Docker image to simplify the process.

  1. Pull and Run the Docker Image:

    # Pull from a domestic mirror
docker pull docker.cnb.cool/tencent/hunyuan/hunyuan-7b:hunyuan-7b-trtllm
# Start the container
docker run --gpus=all -it --rm hunyuaninfer/hunyuan-7b:hunyuan-7b-trtllm

  2. Start the API Service Inside the Container:

    trtllm-serve /path/to/HY-MT1.5-7B \
  --host 0.0.0.0 --port 8000 \
  --backend pytorch \
  --max_batch_size 32 \
  --trust_remote_code

  3. Call Your Translation Service Like OpenAI:

    curl -X POST "http://localhost:8000/v1/chat/completions" \
  -H "Content-Type: application/json" \
  --data '{
    "model": "Hunyuan-MT",
    "messages": [{ "role": "user", "content": "Translate 'Hello, world!' into Chinese." }]
  }'

Option B: Deployment with vLLM (For High Throughput)

If your application needs to handle hundreds or thousands of translation requests simultaneously, vLLM’s high-throughput capability may be more suitable.

  1. Start the vLLM Server (using the 1.8B model as an example):

    python -m vllm.entrypoints.openai.api_server \
    --model tencent/HY-MT1.5-1.8B \
    --trust-remote-code \
    --port 8000 \
    --dtype bfloat16 \
    --tensor-parallel-size 1

  2. Use the Same OpenAI API Format to Call. vLLM also supports quantized model deployment. For example, to start a memory-efficient INT4 quantized model service:

    python -m vllm.entrypoints.openai.api_server \
    --model tencent/HY-MT1.5-1.8B-GPTQ-Int4 \
    --quantization gptq_marlin \
    --trust-remote-code \
    --port 8000

Model Quantization: Making Large Models “Slimmer” and Faster

Deploying the raw model (in BF16 format) can demand significant GPU memory. Quantization technology can dramatically reduce a model’s storage footprint and memory usage while accelerating inference, with minimal loss in accuracy.

The Hunyuan team provides pre-quantized models ready for use:

Model Name Description Estimated GPU Memory Best For
HY-MT1.5-1.8B Original Precision (BF16) ~3.6 GB Scenarios with the highest precision requirements
HY-MT1.5-1.8B-FP8 FP8 Quantized ~1.8 GB Balancing precision and efficiency; a mainstream deployment choice
HY-MT1.5-1.8B-GPTQ-Int4 INT4 Quantized ~0.9 GB Resource constrained edge and mobile device deployment
HY-MT1.5-7B Original Precision (BF16) ~14 GB Server scenarios requiring top-tier translation quality
HY-MT1.5-7B-FP8 FP8 Quantized ~7 GB Lowers the deployment barrier for the 7B model
HY-MT1.5-7B-GPTQ-Int4 INT4 Quantized ~3.5 GB Running the 7B model on consumer-grade GPUs (e.g., RTX 4060)

How to Choose?

  • If you are implementing real-time translation on a mobile or embedded device, HY-MT1.5-1.8B-GPTQ-Int4 is your first choice.
  • If you are deploying on a cloud server and want to balance effect and cost, HY-MT1.5-1.8B-FP8 or HY-MT1.5-7B-FP8 are ideal choices.

Supported Languages: Covering Global Mainstream Languages and Dialects

The Hunyuan Translation Model 1.5 focuses on supporting translation between 33 languages and notably includes support for 5 minority languages/dialects, reflecting its broad application scope.

Language Abbreviation Language Abbreviation
Chinese zh English en
Japanese ja Korean ko
French fr German de
Spanish es Portuguese pt
Russian ru Arabic ar
Traditional Chinese zh-Hant Tibetan bo
Mongolian mn Uyghur ug
Cantonese yue … …

(The complete list includes Italian, Vietnamese, Thai, Hindi, etc., totaling 33 languages.)

Going Further: How to Fine-Tune the Model on Your Own Data?

If you want the model to perform better on your industry-specific terminology or text style, you can perform fine-tuning. Using LLaMA-Factory, an efficient fine-tuning framework, is recommended.

Fine-Tuning Overview:

  1. Prepare Data: Organize your bilingual parallel corpus into a JSON file in the specified sharegpt format.
  2. Setup Environment: Install LLaMA-Factory and specify the use of a Transformers branch compatible with the Hunyuan models. 
    pip install git+https://github.com/huggingface/transformers@4970b23cedaf745f963779b4eae68da281e8c6ca
  3. Run Training: Use the configuration file provided by LLaMA-Factory, specify your model path and data path, and start training. 
    export DISABLE_VERSION_CHECK=1
llamafactory-cli train examples/hunyuan/hunyuan_full.yaml

Through fine-tuning, you can make the Hunyuan translation model more “knowledgeable” about your professional domain.

Summary and Outlook

The release of the Hunyuan Translation Model HY-MT1.5 series provides the industry with a clear technical selection paradigm:

  • Pursuing ultimate quality with sufficient compute? Choose HY-MT1.5-7B.
  • Seeking the perfect balance between effect, speed, and cost? HY-MT1.5-1.8B is undoubtedly the optimal solution today, and its quantized versions further open the door to edge-side AI translation applications.

It is more than just a set of open-source models; it is a complete solution from algorithmic research to production deployment. Whether through simple calls via the Transformers library, high-performance deployment using TensorRT-LLM/vLLM, or personalized fine-tuning, Hunyuan paves the way for developers.

Want to Dive Deeper into Technical Details?

@misc{hunyuan_mt,
      title={Hunyuan-MT Technical Report},
      author={Mao Zheng and Zheng Li and Bingxin Qu and Mingyang Song and Yang Du and Mingrui Sun and Di Wang},
      year={2025},
      eprint={2509.05209},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.05209},
}

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