If you’re also struggling with broken hallucinating translation tools or looking for more powerful model running right on your own machine, you’re going to love this. Tencent has launched Hunyuan-MT-7B, a translation model that’s been making waves in the AI community. It’s designed to handle translations across 33 languages, even covering several of regional ethnic and minority languages like Marathi, Bengali, Cantonese, Polish and many more spoken in several countries like India, China, Europe and worldwide, which is pretty rare in open-source models. And it doesn’t just stop at being versatile, the performance is off the charts. At the WMT25 competition, this model grabbed first place in 30 out of 31 language categories it entered. That’s the kind of track record you usually only see in massive, closed-source systems, but now it’s available for anyone to try. Plus, it comes with a sibling, Hunyuan-MT-Chimera-7B, which is the industry’s first open-source ensemble translation model. Think of it as a super-refiner that takes multiple translation outputs and blends them into the best possible version. Together, these models bring speed, accuracy, and quality that’s hard to beat.
The best thing, these are fully open-source and you can run it right in your machine locally or acclerating it with on-prem/cloud GPUs. Let’s dive into the process.
Prerequisites
The minimum system requirements for running this model are:
- GPU: 1x RTX4090 or 1x RTX A6000
- Storage: 50 GB (preferable)
- VRAM: at least 16 GB
- Anaconda installed
Step-by-step process to install and run Hunyuan-MT 7B
For the purpose of this tutorial, we’ll use a GPU-powered Virtual Machine by NodeShift since it provides high compute Virtual Machines at a very affordable cost on a scale that meets GDPR, SOC2, and ISO27001 requirements. Also, it offers an intuitive and user-friendly interface, making it easier for beginners to get started with Cloud deployments. However, feel free to use any cloud provider of your choice and follow the same steps for the rest of the tutorial.
Step 1: Setting up a NodeShift Account
Visit app.nodeshift.com and create an account by filling in basic details, or continue signing up with your Google/GitHub account.
If you already have an account, login straight to your dashboard.
Step 2: Create a GPU Node
After accessing your account, you should see a dashboard (see image), now:
- Navigate to the menu on the left side.
- Click on the GPU Nodes option.
- Click on Start to start creating your very first GPU node.
These GPU nodes are GPU-powered virtual machines by NodeShift. These nodes are highly customizable and let you control different environmental configurations for GPUs ranging from H100s to A100s, CPUs, RAM, and storage, according to your needs.
Step 3: Selecting configuration for GPU (model, region, storage)
- For this tutorial, we’ll be using 1x RTX A6000 GPU, however, you can choose any GPU as per the prerequisites.
- Similarly, we’ll opt for 200GB storage by sliding the bar. You can also select the region where you want your GPU to reside from the available ones.
Step 4: Choose GPU Configuration and Authentication method
- After selecting your required configuration options, you’ll see the available GPU nodes in your region and according to (or very close to) your configuration. In our case, we’ll choose a 1x RTX A6000 48GB GPU node with 64vCPUs/63GB RAM/200GB SSD.
2. Next, you’ll need to select an authentication method. Two methods are available: Password and SSH Key. We recommend using SSH keys, as they are a more secure option. To create one, head over to our official documentation.
Step 5: Choose an Image
The final step is to choose an image for the VM, which in our case is Nvidia Cuda.
That’s it! You are now ready to deploy the node. Finalize the configuration summary, and if it looks good, click Create to deploy the node.
Step 6: Connect to active Compute Node using SSH
- As soon as you create the node, it will be deployed in a few seconds or a minute. Once deployed, you will see a status Running in green, meaning that our Compute node is ready to use!
- Once your GPU shows this status, navigate to the three dots on the right, click on Connect with SSH, and copy the SSH details that appear.
As you copy the details, follow the below steps to connect to the running GPU VM via SSH:
- Open your terminal, paste the SSH command, and run it.
2. In some cases, your terminal may take your consent before connecting. Enter ‘yes’.
3. A prompt will request a password. Type the SSH password, and you should be connected.
Output:
Next, If you want to check the GPU details, run the following command in the terminal:
!nvidia-smi
Step 7: Set up the project environment with dependencies
- Create a virtual environment using Anaconda.
conda create -n hunyuan python=3.11 -y && conda activate hunyuan
Output:
2. Install required dependencies.
pip install transformers==v4.56.0
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
pip install einops timm pillow
pip install git+https://github.com/huggingface/accelerate
pip install git+https://github.com/huggingface/diffusers
pip install huggingface_hub
pip install sentencepiece bitsandbytes protobuf decord numpy
Output:
3. Install and run jupyter notebook.
conda install -c conda-forge --override-channels notebook -y
conda install -c conda-forge --override-channels ipywidgets -y
jupyter notebook --allow-root
4. If you’re on a remote machine (e.g., NodeShift GPU), you’ll need to do SSH port forwarding in order to access the jupyter notebook session on your local browser.
Run the following command in your local terminal after replacing:
<YOUR_SERVER_PORT> with the PORT allotted to your remote server (For the NodeShift server – you can find it in the deployed GPU details on the dashboard).
<PATH_TO_SSH_KEY> with the path to the location where your SSH key is stored.
<YOUR_SERVER_IP> with the IP address of your remote server.
ssh -L 8888:localhost:8888 -p <YOUR_SERVER_PORT> -i <PATH_TO_SSH_KEY> root@<YOUR_SERVER_IP>
Output:
After this copy the URL you received in your remote server:
And paste this on your local browser to access the Jupyter Notebook session.
Step 8: Download and Run the model
- Open a Python notebook inside Jupyter.
2. Download the model checkpoints.
from transformers import AutoModelForCausalLM, AutoTokenizer
import os
model_name_or_path = "tencent/Hunyuan-MT-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, device_map="auto")
Output:
3. Run the model for inference.
messages = [
{
"role": "user",
"content": """Translate the following sentence into the following languages: Chinese (zh), English (en), Spanish (es), Hindi (hi), Arabic (ar), French (fr), Russian (ru), Portuguese (pt), Bengali (bn), and Japanese (ja).
Provide only the translations, each on a new line, prefixed with the language name.
Sentence:
"Technology connects people across the world, making communication faster, easier, and more accessible than ever before."
"""
}
]
tokenized_chat = tokenizer.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=False,
return_tensors="pt"
)
outputs = model.generate(tokenized_chat.to(model.device), max_new_tokens=2048)
output_text = tokenizer.decode(outputs[0])
print(output_text)
Output:
Conclusion
Hunyuan-MT-7B bring state-of-the-art translation capabilities to your fingertips, covering 33 languages, delivering benchmark-topping accuracy, and offering the flexibility to run locally on your machine. What once felt limited to big, closed-source systems is now open and accessible for developers, researchers, and language enthusiasts everywhere. And with NodeShift, you don’t have to worry about the heavy lifting of setup or GPU scaling, whether you’re testing small projects or deploying large workloads, NodeShift is making these AI models more accessible and readily available for you to run both on cloud or on premises for your experiments or enterprise integrations.
