gemma3-multilingual-translator-270m

Gemma3 Multilingual Translator (270M)

A multilingual translation model based on Gemma3-270m, fine-tuned with LoRA and Bias-corrected EMA on 7.4M translation pairs.

Features

• 6-way translation: Korean, English, Japanese (all directions)
• High quality: 60% loss reduction (4.03 → 1.59) with EMA smoothing
• Easy to use: Simple prompt format with language tags
• ONNX included: Pre-converted ONNX models for PyTorch-free deployment
• Multiple precisions: fp32 and q4 quantized ONNX models included

Quick Start

Using Transformers (PyTorch)

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model
model = AutoModelForCausalLM.from_pretrained(
    "sappho192/gemma3-multilingual-translator-270m",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("sappho192/gemma3-multilingual-translator-270m")

# Translate Korean to English
prompt = "<src:ko><tgt:en>\n안녕하세요, 만나서 반갑습니다.\n###\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=128,
        do_sample=False,
        pad_token_id=tokenizer.pad_token_id
    )

result = tokenizer.decode(outputs[0], skip_special_tokens=True)
translation = result.split("###")[-1].strip()
print(translation)  # "Hello, nice to meet you."

Using ONNX (No PyTorch Required)

from torch_free.inference import TranslatorInferencer

# Load ONNX model (q4 recommended for speed/size)
translator = TranslatorInferencer(
    "sappho192/gemma3-multilingual-translator-270m",
    precision="q4"
)

# Translate
result = translator.translate(
    "안녕하세요, 만나서 반갑습니다.",
    src_lang="ko",
    tgt_lang="en"
)
print(result)  # "Hello, nice to meet you."

Translation Format

<src:SOURCE_LANG><tgt:TARGET_LANG>
SOURCE_TEXT
###

Language codes: ko (Korean), en (English), ja (Japanese)

Examples

Model Details

Training Specifications

Model Architecture

Included Files

ONNX Model Sizes

Requirements

For PyTorch Inference

• Python 3.11+
• PyTorch 2.0+
• transformers >= 4.55.0

For ONNX Inference (No PyTorch)

• Python 3.11+
• numpy >= 1.24.0
• onnxruntime >= 1.16.0
• tokenizers >= 0.15.0

License

Apache License 2.0

Citation

If you use this model, please cite:

@misc{gemma3-multilingual-translator,
  author = {Taein Kim},
  title = {Gemma3 Multilingual Translator},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/sappho192/gemma3-multilingual-translator-270m}
}