cyberagent/CAT-Translate-3.3b

CAT-Translate 🐱

 

Tiny Language Model For Japanese and English Bidirectional Translation

• —Purrs on your lap 🐱: Small and efficient! 0.8-7B models that run on edge devices.
• —Swift and Feline Sharp 🐾: Beats TranslateGemma-12B on text-to-text translation quality.
• —Adopt and adapt 🐈: Open source (MIT License) models you can customize and extend.

<div align="center"> <img src="CAT-logo.png" alt="Cat sleeping on top of a laptop." width="200"> </div>

Models

All models are available on Hugging Face:

• —CAT-Translate-0.8B
• —CAT-Translate-1.4B
• —CAT-Translate-3.3B
• —CAT-Translate-7B

Evaluation

We conducted evaluation on the translation subsets of the following benchmarks:

• —The Business Scene Dialogue corpus (BSD)
• —Each conversation is given to the model to translate instead of each sentence.
• —Court Interpreter (Court)
• —JMedBench (JMed)
• —ejmmt subsets are used.
• —pfmt-bench-fin-ja (PFMT)
• —WAT 2025 Patent Translation (wat-pat-2025)

We chose these tasks as benchmarks because (1) they are derived from real world applications and (2) are less overoptimized compared to popular datasets (e.g., WMT).

The results are below. All the models achieved the best scores among all models (including closed source) within their respective sizes for both En-Ja and Ja-En translation tasks.

A detailed experimental evaluation will be present in a technical report.

Usage

The model supports English to Japanese and Japanese to English translation with the following prompt format:

from transformers import pipeline

# Load the model
chat_pipeline = pipeline("text-generation", model="CyberAgent/CAT-Translate-3.3b")

# Define the prompt template
prompt = "Translate the following {src_lang} text into {tgt_lang}.\n\n{src_text}"

# Example: Japanese to English
src_lang = "Japanese"
tgt_lang = "English"
src_text = "🐈はとてもかわいいの。おててがまるくてふわふわなの。"

user_input = [{"role": "user", "content": prompt.format(src_lang=src_lang, tgt_lang=tgt_lang, src_text=src_text)}]

response = chat_pipeline(user_input, max_new_tokens=512)

print("-" * 20)
print("Source Text:")
print(src_text)
print("Translation:")
print(response[0]['generated_text'][-1]['content'])

Important: You need to apply the chat template to run the model correctly. The template is the same as sarashina2.2-3b-instruct-v0.1.

Why Use Instructions?

Although the model is specialized for machine translation, we require an instruction prompt to invoke the translation capability. This design choice provides better customizability—extending and merging this model is easier this way. Since the model is open source, any extensions are welcome!

Training

We used the sarashina2.2 series (MIT LICENSE) as our pretrained model. While Qwen-3 showed higher benchmark scores, we found that sarashina generated more natural Japanese text that avoided "translationese" patterns. We hypothesized that naturalness is more difficult to learn than translation accuracy, leading us to choose sarashina as our base model.

Our training process involved:

• —Synthesizing parallel corpora from monolingual data using large language models
• —Two-stage supervised fine-tuning (SFT) approach
• —Reinforcement learning with Multi-Objective GRPO (Ichihara et al. 2025)
• —LoRA for efficient training

For detailed information about our training methodology, data preparation, and technical specifications, please see TRAINING.md.

License

The model is licensed under the MIT License.

Citation

@misc{jinnai2026cattranslatebuildingcompactopensource,
      title={CAT-Translate: Building Compact Open-Source Models for Japanese-English Translation}, 
      author={Yuu Jinnai},
      year={2026},
      eprint={2606.21413},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2606.21413}, 
}

Acknowledgments

This project stands on the shoulders of giants. In particular, the following resources significantly helped us develop the model:

• —sarashina by SB Intuitions
• —gpt-oss by OpenAI
• —MetricX by Juraj Juraska et al.
• —Duplodocus by AllenAI
• —fastText by Facebook Research
• —COMET by Ricardo Rei et al.
• —sacrebleu by Matt Post
• —Mitsuki Sakamoto for deploying the model with UI for internal testing