yanolja / YanoljaNEXT-Rosetta-4B

YanoljaNEXT-Rosetta-4B

This model is a fine-tuned version of google/gemma-3-4b-pt . As it is intended solely for text generation, we have extracted and utilized only the Gemma3ForCausalLM component from the original architecture.

Different to our successor EEVE, this model does not feature an expanded tokenizer.

• Model Name: yanolja/YanoljaNEXT-Rosetta-4B
• Base Model: google/gemma-3-4b-pt

Model Description

This model is a 4-billion parameter, decoder-only language model built on the Gemma3 architecture and fine-tuned by Yanolja NEXT. It is specifically designed to translate structured data (JSON format) while preserving the original data structure.

The model was trained on a multilingual dataset covering the following languages:

• English
• Spanish
• French
• German
• Portuguese
• Japanese
• Korean
• Chinese
• Arabic
• Russian
• Hindi

While optimized for these languages, it may also perform effectively on other languages supported by the base Gemma3 model.

How to use

You can use this model with the transformers library as follows:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "yanolja/YanoljaNEXT-Rosetta-4B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16
)

# Example prompt
target_language = "Korean"
messages = [
    {"role": "system", "content": f"Translate the user's text to {target_language}.\nContext: Technical support conversation about USB modem configuration\nTone: Informative and helpful\nGlossary:\n- USB modem -> USB 모뎀\n- configuration -> 설정\n- technical support -> 기술 지원\nProvide the final translation immediately without any other text."},
    {"role": "user", "content": "Yanolja NEXT is a company that provides global cutting-edge technology for the hospitality industry."}
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=4096)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

The model outputs the final translation in JSON format when appropriate, or plain text for simple translations.

Training Procedure

Training Data

The translation datasets were compiled from several sources, including:

• AI Hub
• Europarl

The model was fine-tuned on multilingual translation data to optimize performance across the supported language pairs.

Performance

Translation Quality Benchmarks

The following CHrF++ scores demonstrate the model's competitive performance against other state-of-the-art translation models on English to Korean translation:

YanoljaNEXT-Rosetta-4B achieves competitive translation quality while maintaining efficiency as a 4B parameter model.

Intended Uses & Limitations

This model is intended for translating structured data (JSON format) while preserving the original structure. It is particularly well-suited for tasks such as localizing product catalogs, translating hotel reviews, or handling any other structured content that requires accurate translation.

Limitations

The model's primary focus is on JSON data. Performance on unstructured text or other data formats may vary.

License

This model is released under the Gemma license, inherited from its base model, google/gemma-3-4b-pt . Please consult the official Gemma license terms for detailed usage guidelines.

Citation

If you use this model, please consider citing:

@misc{yanolja2025yanoljanextrosetta,
  author = {Yanolja NEXT Co., Ltd.},
  title = {YanoljaNEXT-Rosetta-4B},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  howpublished = {\\url{https://huggingface.co/yanolja/YanoljaNEXT-Rosetta-4B}}
}

References

This work utilizes several models and datasets. We would like to acknowledge the original authors for their valuable contributions to the field.

@misc{gemma3,
  author = {Google},
  title = {Gemma 3},
  year = {2024},
  publisher = {Google DeepMind},
  howpublished = {\\url{https://deepmind.google/models/gemma/gemma-3/}}
}


@misc{aihub,
  author = {National Information Society Agency (NIA)},
  title = {AI-Hub: AI Integrated Platform},
  year = {2025},
  publisher = {National Information Society Agency},
  howpublished = {\\url{https://aihub.or.kr}}
}

@article{europarl,
  author    = {Koehn, Philipp},
  title     = {Europarl: A Parallel Corpus for Statistical Machine Translation},
  journal   = {MT Summit},
  year      = {2005},
  pages     = {79--86}
}