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yanolja/
YanoljaNEXT-Rosetta-4B

Name: YanoljaNEXT-Rosetta-4B
Availability: InStock

text-generation-inference

endpoints_compatible

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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 from our previous EEVE models, 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:

python

import json
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "yanolja/YanoljaNEXT-Rosetta-4B"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
    max_memory={0: "23GB"},
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

target_language = "Korean"
context = {
  "context": "Simple introduction about a tech company.",
  "tone": "Informative and helpful",
  "glossary": {
    "Yanolja NEXT": "야놀자넥스트",
    "travel industry": "여행 산업",
  }
}

system = [f"Translate the user's text to {target_language}."]
for key, value in context.items():
  key_pascal = key.capitalize()
  if isinstance(value, dict):
    system.append(f"{key_pascal}:")
    for f, t in value.items():
      system.append(f"- {f} -> {t}")
  else:
    system.append(f"{key_pascal}: {value}")

system.append("Provide the final translation immediately without any other text.")

source = {
  "company_name": "Yanolja NEXT",
  "description": "Yanolja NEXT is a company that provides cutting-edge "
                 "technology for the global travel industry.",
}

messages = [
    {"role": "system", "content": "\n".join(system)},
    {"role": "user", "content": json.dumps(source, ensure_ascii=False)},
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print(prompt)
# <bos><start_of_turn>instruction
# Translate the user's text to Korean.
# Context: Simple introduction about a tech company.
# Tone: Informative and helpful
# Glossary:
# - Yanolja NEXT -> 야놀자넥스트
# - travel industry -> 여행 산업
# Provide the final translation immediately without any other text.<end_of_turn>
# <start_of_turn>source
# {"company_name": "Yanolja NEXT", "description": "Yanolja NEXT is a company that provides cutting-edge technology for the global travel industry."}<end_of_turn>
# <start_of_turn>translation

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
input_length = inputs["input_ids"].shape[1]

with torch.inference_mode():
    outputs = model.generate(
        **inputs,
        max_new_tokens=64,
    )

generated_tokens = outputs[0][input_length:]
translation = tokenizer.decode(generated_tokens, skip_special_tokens=True)

print(json.dumps(json.loads(translation), indent=2, ensure_ascii=False))
# {
#   "company_name": "야놀자넥스트",
#   "description": "야놀자넥스트는 글로벌 여행 산업에 최첨단 기술을 제공하는 회사입니다."
# }

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:

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

Language	Portion (%)	Language	Portion (%)
Korean	24.2	French	2.8
English	16.2	German	2.5
Japanese	5.8	Russian	2.4
Italian	5.3	Arabic	2.3
Chinese	4.4	Other	30.2
Spanish	3.9

Performance

Translation Quality Benchmarks

The following CHrF++ scores (WMT24++) demonstrate the model's competitive performance compared to other state-of-the-art translation models on English to Korean translation:

Model	CHrF++ Score (WMT24++)
yanolja/YanoljaNEXT-Rosetta-12B	34.75
yanolja/YanoljaNEXT-Rosetta-20B	33.87
google/gemini-2.0-flash-001	33.81
openai/gpt-oss-120b	31.51
yanolja/YanoljaNEXT-Rosetta-4B	31.31
openai/gpt-4.1-nano	31.15
Qwen/Qwen3-235B-A22B-Instruct-2507-FP8	31.02
openai/gpt-oss-20b	30.56
google/gemma-3-27b-it	30.05
google/gemma-3-4b-pt	27.53

YanoljaNEXT-Rosetta-4B achieves competitive translation quality while maintaining the efficiency of 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}
}