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
| Direction | Input | Output |
|---|
| KO→EN | 안녕하세요, 만나서 반갑습니다. | Hello, nice to meet you. |
| EN→KO | Hello, nice to meet you. | 안녕하세요, 만나서 반갑습니다. |
| JA→KO | こんにちは、お元気ですか？ | 안녕하세요, 건강하세요? |
| KO→JA | 오늘 날씨가 정말 좋네요. | 今日の天気は本当にいいですね。 |
Model Details
Training Specifications
| Parameter | Value |
|---|
| Base Model | google/gemma-3-270m |
| Training Data | 7.4M translation pairs |
| LoRA Rank | 16 |
| LoRA Alpha | 32 |
| EMA Decay | 0.999 |
| Final Loss | 1.59 |
Model Architecture
| Parameter | Value |
|---|
| Hidden Size | 640 |
| Intermediate Size | 2048 |
| Num Layers | 18 |
| Num Attention Heads | 4 |
| Num KV Heads | 1 |
| Head Dim | 256 |
| Vocab Size | 262,144 |
| Max Position Embeddings | 32,768 |
Included Files
| File | Description |
|---|
model.safetensors | Merged model weights (LoRA + base) |
onnx/model.onnx | ONNX model (fp32) |
onnx/model_q4.onnx | ONNX model (int4 quantized) |
torch_free/ | PyTorch-free inference code |
ONNX Model Sizes
| Precision | Model Size | Avg. Latency |
|---|
| fp32 | 1.1 GB | 0.26s |
| q4 | 764 MB | 0.17s |
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}
}