TranslateGemma Complete Guide: Benchmarks, Setup & Usage (2026)

Published on January 19, 2026

TranslateGemma is a family of lightweight, state-of-the-art open-weight translation models released by Google on January 15, 2026. Built on the Gemma 3 foundation models, TranslateGemma is optimized for translation tasks across 55 languages, including high-, mid-, and low-resource languages. It supports both text translation and multimodal translation (e.g., text within images) without specific fine-tuning for images.

The models come in three sizes—4B, 12B, and 27B parameters—making them suitable for deployment on everything from mobile devices to cloud environments.

Key Highlights

• Efficiency: The 12B model outperforms the baseline Gemma 3 27B on the WMT24++ benchmark using MetricX, achieving high quality with fewer parameters.
• Foundation: Built on Gemma 3 (2B, 4B, 12B, 27B) instruction-tuned base models, retaining multimodal capabilities.
• Training: Trained using a mix of human-translated data and synthetic data generated by Gemini models, followed by a reinforcement learning (RL) phase using an ensemble of reward models (MetricX, Gemma-AutoMQM) to refine quality.
• Multimodal Capabilities: Integrates a SigLIP (Sigmoid Loss for Language-Image Pre-training) vision encoder for handling images (resized to 896x896 pixels, tokenized to 256 tokens).
• License: Open under Google’s Gemma Terms of Use; requires acceptance on Hugging Face for access.
• Official Report: Detailed in the arXiv technical report 2601.09012.
• Current Release: As of January 19, 2026, models are available on Hugging Face, Kaggle, and Vertex AI.

Features

• Language Support: Core evaluation on 55 language pairs (e.g., English to Spanish, French, Chinese, Hindi, Swahili, Estonian). Trained with synthetic data for these 55 plus 30 additional pairs for fine-tuning potential.
• Input Formats: Text or images (via URL or local path) using the inherited Gemma 3 multimodal architecture.
• Output: Natural, contextually accurate translations.
• Deployment Flexibility: From edge devices (4B) to cloud (27B).
• No Safety Filters: Models lack built-in safety mechanisms and are “open-weight”; users must implement their own filters/guardrails for production use.
• Limitations: May struggle with sarcasm, nuanced idioms, or non-translation tasks within a chat context. Potential biases from training data. Performance on languages beyond the core 55 may vary.

Model Variants and Requirements

Estimates below are for quantized versions (e.g., 4-bit) which are standard for local use. Full precision requires 2-4x more memory.

[!NOTE] Note on Parameters: The nominal parameter counts (4B, 12B, 27B) refer to the text model. Total parameters including the vision encoder (SigLIP) may be slightly higher.

Performance: Use NVIDIA GPUs with CUDA for best performance. AMD/Intel GPUs may work experimentally via ROCm or oneAPI but are less standard.

Context Window: Input context limit is 2K tokens (fine-tuning limit during training). Models can technically process longer contexts but quality may degrade beyond 2K tokens. Ollama versions may advertise larger contexts (e.g., 128K from base Gemma 3), but rigorous testing for translation quality beyond 2K is recommended.

Setup Instructions

TranslateGemma uses the Hugging Face transformers library.

Prerequisites

• Python 3.10+
• Hugging Face Account: Accept the license on the model page.
• Libraries: pip install transformers torch accelerate bitsandbytes pillow

OS-Specific Setup

Windows

1. Install Git: Download from git-scm.com.
2. GPU Setup: Install NVIDIA CUDA Toolkit (v12+) from developer.nvidia.com. Verify with nvidia-smi.
3. Create Environment:

   python -m venv translategemma_env
   .\translategemma_envScriptsactivate

4. Install Dependencies:

   pip install -U transformers torch accelerate bitsandbytes pillow

5. Log in: huggingface-cli login (paste your access token).

[!TIP] If using WSL (Windows Subsystem for Linux), follow the Linux/Unix steps inside your Ubuntu instance for a smoother experience.

macOS (Apple Silicon)

1. Install Homebrew:

   /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

2. Install Python: brew install python.
3. Activate Environment:

   python3 -m venv translategemma_env
   source translategemma_env/bin/activate

4. Install Dependencies:

   # Standard installation includes MPS support for Apple Silicon
   pip install torch torchvision torchaudio
   pip install transformers accelerate pillow

Linux/Unix

1. Install Python: sudo apt update && sudo apt install python3-venv python3-pip.
2. GPU Setup: Install CUDA (e.g., sudo apt install nvidia-cuda-toolkit).
3. Setup Environment:

   python3 -m venv translategemma_env
   source translategemma_env/bin/activate
   pip install -U transformers torch accelerate bitsandbytes pillow

Docker Implementation

You can run TranslateGemma using Ollama (which has Docker support) or a custom container.

Option A: Ollama (Recommended for Local Use)

Ollama provides the easiest way to run quantized versions locally without complex Python setup.

1. Install Ollama: Download from ollama.com.
2. Run Model:

   ollama run translategemma:12b

   (Check the Ollama library for the exact tag availability, e.g., :27b).

Option B: Custom Dockerfile

Run your own API using this verified Dockerfile. This assumes you have an app.py script (like the one below) in the same directory. (Note: You’ll typically need to pass your HF token implicitly or ensure the cache volume is mounted if using gated models directly within Docker).

Dockerfile:

FROM nvidia/cuda:12.1.0-cudnn8-runtime-ubuntu22.04
# Install basic tools
RUN apt-get update && apt-get install -y python3 python3-pip git && 
    rm -rf /var/lib/apt/lists/*
WORKDIR /app
# Install ML dependencies
RUN pip3 install --no-cache-dir torch transformers accelerate bitsandbytes fastapi uvicorn pillow
# Copy your application code
COPY app.py .
# Run the API server
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"]

Sample app.py (Basic FastAPI Endpoint):

from fastapi import FastAPI
from transformers import pipeline
import torch

app = FastAPI()

# Initialize pipeline with 4-bit quantization support if desired by adding proper config
pipe = pipeline("image-text-to-text", model="google/translategemma-4b-it", device="cuda" if torch.cuda.is_available() else "cpu", torch_dtype=torch.bfloat16)

@app.post("/translate")
def translate(data: dict):
    # Expects data={"text": "..."} or setup for image
    messages = [{"role": "user", "content": [data]}]
    output = pipe(text=messages, max_new_tokens=200, generate_kwargs={"do_sample": False})
    return {"translation": output[0]["generated_text"][-1]["content"]}

Build & Run:

docker build -t translategemma .
docker run --gpus all -p 8000:8000 translategemma

Usage Examples

Text Translation (Python)

This example uses the 4B model. Replace google/translategemma-4b-it with 12b-it or 27b-it as needed.

from transformers import pipeline
import torch
# Initialize pipeline
# Use device="cuda" for NVIDIA GPU, "mps" for Mac M1/M2, or "cpu"
device = "cuda" if torch.cuda.is_available() else "cpu"
if torch.backends.mps.is_available():
    device = "mps"
pipe = pipeline(
    "image-text-to-text",
    model="google/translategemma-4b-it",
    device=device,
    torch_dtype=torch.bfloat16,
)
# Text Input
messages = [
    {"role": "user", "content": [
        {"type": "text", "source_lang_code": "en", "target_lang_code": "es", "text": "Hello, how are you?"}
    ]}
]
# Generate
# max_new_tokens controls translation length
output = pipe(text=messages, max_new_tokens=200, generate_kwargs={"do_sample": False})
print(output[0]["generated_text"][-1]["content"])
# Output: "Hola, ¿cómo estás?"

Image Translation (Multimodal)

TranslateGemma can natively read text from images and translate it.

# Image Input
messages = [
    {"role": "user", "content": [
        {"type": "image", "source_lang_code": "en", "target_lang_code": "de", "url": "https://example.com/image_with_text.jpg"}
    ]}
]
output = pipe(text=messages, max_new_tokens=200, generate_kwargs={"do_sample": False})
print(output[0]["generated_text"][-1]["content"])

Benchmarks

TranslateGemma excels on WMT24++ (text) and Vistra (image) benchmarks. Lower MetricX scores indicate better performance (less error).

WMT24++ (Text Translation)

Averaged across 55 language pairs.

Key Insight: The 12B TranslateGemma beats the larger 27B Gemma 3 Baseline, making it highly efficient.

Vistra (Image Translation)

Averaged performance on multimodal translation tasks.

Where to Test It

If you want to try TranslateGemma before installing:

1. Google Colab: Official Example Notebook (Requires Hugging Face token).
2. Kaggle: Official TranslateGemma Model Page.
3. Ollama: Run locally with ollama run translategemma:27b.
4. Vertex AI: Available in the Google Cloud Vertex AI Model Garden.

Additional Tips

• Fine-Tuning: For low-resource languages not in the core 55, use PEFT (Parameter-Efficient Fine-Tuning) to adapt the model with minimal data. LoRA and QLoRA are particularly effective for translation fine-tuning.
• Ethical Use: As open-weight models, be aware of potential biases from training data. Implement necessary safety filters for your specific use case, especially for user-facing applications.
• Troubleshooting: If you encounter Out-Of-Memory (OOM) errors, try enabling 4-bit quantization (load_in_4bit=True in Transformers) or switching to a smaller model (e.g., 27B → 12B).
• License: Review the Google Gemma Terms of Use before deploying in production. Generally permits research and commercial use with some restrictions.
• Updates: Always check the Hugging Face model card for the latest revisions. This guide is current as of January 19, 2026.

Comparison with Other Translation Solutions