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Model: cyberagent/CAT-Translate-7b Source: Original Platform
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LICENSE
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LICENSE
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MIT License
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Copyright (c) 2026 CyberAgent AI Lab
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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---
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license: mit
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language:
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- ja
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- en
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pipeline_tag: translation
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library_name: transformers
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tags:
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- translation
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- machine-translation
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- japanese
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- english
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datasets:
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- cyberagent/CAT-Translate-Dataset
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---
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# CAT-Translate 🐱
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[](https://opensource.org/licenses/MIT)
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[](https://huggingface.co/cyberagent/CAT-Translate-7b/)
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Tiny Language Model For Japanese and English Bidirectional Translation
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- **Purrs on your lap** 🐱: Small and efficient! 0.8-7B models that run on edge devices.
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- **Swift and Feline Sharp** 🐾: Beats TranslateGemma-12B on text-to-text translation quality.
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- **Adopt and adapt** 🐈: Open source (MIT License) models you can customize and extend.
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<div align="center">
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<img src="CAT-logo.png" alt="Cat sleeping on top of a laptop." width="200">
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</div>
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## Models
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All models are available on Hugging Face:
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- [CAT-Translate-0.8B](https://huggingface.co/cyberagent/CAT-Translate-0.8b/)
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- [CAT-Translate-1.4B](https://huggingface.co/cyberagent/CAT-Translate-1.4b/)
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- [CAT-Translate-3.3B](https://huggingface.co/cyberagent/CAT-Translate-3.3b/)
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- [CAT-Translate-7B](https://huggingface.co/cyberagent/CAT-Translate-7b/)
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## Evaluation
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We conducted evaluation on the translation subsets of the following benchmarks:
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- [The Business Scene Dialogue corpus](https://github.com/tsuruoka-lab/BSD) (BSD)
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- Each conversation is given to the model to translate instead of each sentence.
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- [Court Interpreter](https://github.com/mynlp/court_interpreter) (Court)
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- [JMedBench](https://huggingface.co/datasets/Coldog2333/JMedBench) (JMed)
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- ejmmt subsets are used.
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- [pfmt-bench-fin-ja](https://github.com/pfnet-research/pfmt-bench-fin-ja) (PFMT)
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- [WAT 2025 Patent Translation](https://sites.google.com/view/pat-claims-trans-2025/) (wat-pat-2025)
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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).
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The results are below.
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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.
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| Model | Avg. BLEU | Avg. BLEU Ja->En | Avg. BLEU En->Ja | BSD (Ja-En) | Court (Ja-En) | JMed (Ja-En) | PFMT (Ja-En) | wat-pat-2025 (Ja-En) | BSD (En-Ja) | JMed (En-Ja) | PFMT (En-Ja) | wat-pat-2025 (En-Ja) |
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|:-------------------------------------------------|----------:|-----------------:|-----------------:|------------:|--------------:|-------------:|-------------:|------------------:|------------:|-------------:|-------------:|------------------:|
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| CyberAgent/CAT-Translate-7B | 37.68 | 41.06 | 34.31 | 33.75 | 45.29 | 30.65 | 49.86 | 45.74 | 16.29 | 29.62 | 52.94 | 38.37 |
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| CyberAgent/CAT-Translate-3.3B | 36.16 | 37.51 | 34.80 | 26.51 | 42.44 | 24.47 | 49.93 | 44.23 | 17.21 | 28.67 | 53.88 | 39.44 |
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| CyberAgent/CAT-Translate-1.4B | 33.73 | 33.26 | 34.19 | 31.28 | 43.84 | 24.08 | 36.55 | 30.57 | 15.71 | 26.92 | 51.53 | 42.58 |
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| Unbabel/Tower-Plus-9B | 32.41 | 36.84 | 27.99 | 15.43 | 40.54 | 29.13 | 58.00 | 41.10 | 10.00 | 18.80 | 53.00 | 30.16 |
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| google/translategemma-12b-it | 32.24 | 35.81 | 28.68 | 31.58 | 34.30 | 23.46 | 48.75 | 40.97 | 15.92 | 21.79 | 52.53 | 24.47 |
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| CyberAgent/CAT-Translate-3.3B-beta | 30.60 | 30.32 | 30.88 | 17.20 | 38.65 | 23.96 | 40.58 | 31.22 | 16.63 | 26.68 | 53.40 | 26.80 |
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| CyberAgent/CAT-Translate-0.8B | 30.42 | 29.71 | 30.68 | 29.63 | 33.19 | 22.96 | 32.51 | 30.56 | 14.60 | 26.22 | 50.62 | 32.87 |
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| google/translategemma-4b-it | 28.09 | 29.41 | 26.76 | 28.86 | 25.89 | 21.50 | 42.65 | 28.16 | 14.14 | 20.68 | 51.99 | 20.23 |
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| LiquidAI/LFM2.5-1.2B-JP | 25.47 | 24.51 | 26.43 | 19.06 | 29.99 | 22.10 | 43.61 | 7.80 | 14.57 | 23.85 | 54.77 | 12.54 |
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| pfnet/plamo-2-translate | 25.24 | 25.92 | 24.57 | 25.55 | 28.63 | 22.90 | 29.02 | 23.48 | 17.35 | 24.98 | 32.04 | 23.89 |
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| LiquidAI/LFM2-350M-ENJP-MT | 24.95 | 24.91 | 25.00 | 10.94 | 29.56 | 21.48 | 41.40 | 21.17 | 8.11 | 22.84 | 47.53 | 21.52 |
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| mistralai/Ministral-8B-Instruct-2410 | 24.12 | 27.52 | 20.71 | 19.23 | 29.21 | 16.25 | 50.23 | 22.69 | 12.91 | 16.49 | 41.66 | 11.80 |
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| nvidia/NVIDIA-Nemotron-Nano-9B-v2-Japanese | 22.97 | 22.77 | 23.18 | 9.62 | 34.98 | 18.01 | 38.44 | 12.81 | 10.62 | 20.41 | 42.55 | 19.13 |
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| Rakuten/RakutenAI-2.0-mini-instruct | 18.43 | 17.24 | 19.62 | 0.11 | 30.62 | 18.21 | 29.34 | 7.90 | 5.19 | 20.36 | 45.70 | 7.23 |
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| SakanaAI/TinySwallow-1.5B-Instruct | 15.74 | 14.99 | 16.49 | 4.96 | 18.93 | 15.83 | 26.67 | 8.58 | 6.30 | 17.58 | 34.07 | 8.00 |
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| llm-jp/llm-jp-3.1-1.8b-instruct4 | 15.18 | 16.26 | 14.11 | 18.82 | 2.44 | 15.67 | 30.65 | 13.72 | 15.38 | 4.91 | 25.47 | 10.65 |
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| tencent/HY-MT1.5-1.8B | 14.49 | 8.95 | 20.04 | 5.50 | 4.59 | 4.00 | 15.67 | 14.98 | 6.33 | 18.13 | 37.75 | 17.96 |
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| shisa-ai/shisa-v2.1-llama3.2-3b | 14.27 | 14.26 | 14.28 | 17.08 | 3.70 | 8.26 | 26.86 | 15.42 | 13.18 | 5.54 | 25.97 | 12.41 |
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| google/gemma-2-2b-jpn-it | 14.15 | 16.98 | 11.32 | 20.04 | 8.08 | 11.27 | 31.49 | 14.01 | 12.37 | 4.48 | 16.24 | 12.21 |
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| shisa-ai/shisa-v2.1-lfm2-1.2b | 13.08 | 14.02 | 12.14 | 20.93 | 4.95 | 7.68 | 26.72 | 9.80 | 12.11 | 5.54 | 17.60 | 13.30 |
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| microsoft/phi-4 | 11.92 | 13.48 | 10.36 | 6.10 | 18.66 | 2.81 | 24.86 | 14.98 | 3.24 | 6.97 | 14.36 | 16.87 |
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| tencent/HY-MT1.5-7B | 10.56 | 13.46 | 7.67 | 4.99 | 12.32 | 5.72 | 29.53 | 14.76 | 0.82 | 7.80 | 14.30 | 7.74 |
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| tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5 | 10.35 | 12.42 | 8.28 | 24.25 | 2.30 | 3.69 | 14.11 | 17.74 | 6.82 | 2.37 | 11.21 | 12.71 |
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| Qwen/Qwen2.5-14B-Instruct | 8.39 | 9.88 | 6.89 | 10.81 | 4.70 | 4.27 | 11.18 | 18.46 | 4.01 | 3.69 | 13.42 | 6.42 |
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| meta-llama/Llama-3.2-3B-Instruct | 6.06 | 9.90 | 2.23 | 18.60 | 0.41 | 2.72 | 16.62 | 11.17 | 1.44 | 1.10 | 4.50 | 1.87 |
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A detailed experimental evaluation will be present in a technical report.
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## Usage
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The model supports English to Japanese and Japanese to English translation with the following prompt format:
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```python
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from transformers import pipeline
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# Load the model
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chat_pipeline = pipeline("text-generation", model="CyberAgent/CAT-Translate-7b")
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# Define the prompt template
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prompt = "Translate the following {src_lang} text into {tgt_lang}.\n\n{src_text}"
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# Example: Japanese to English
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src_lang = "Japanese"
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tgt_lang = "English"
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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。"
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user_input = [{"role": "user", "content": prompt.format(src_lang=src_lang, tgt_lang=tgt_lang, src_text=src_text)}]
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response = chat_pipeline(user_input)
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print("-" * 20)
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print("Source Text:")
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print(src_text)
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print("Translation:")
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print(response[0]['generated_text'][-1]['content'])
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```
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**Important**: You need to apply the chat template to run the model correctly. Note that the chat template of 7B model is different from the other CAT-Translate models.
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### Why Use Instructions?
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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!
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## Training
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This 7B model is based on CyberAgent's in-house model, developed by [Ryosuke Ishigami](https://huggingface.co/rishigami).
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Our training process involved:
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- Synthesizing datasets using large language models (e.g., gpt-oss)
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- Multi-stage supervised fine-tuning (SFT) approach
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- Reinforcement learning with [Multi-Objective GRPO (Ichihara et al. 2025)](https://arxiv.org/abs/2509.22047)
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## License
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The model is licensed under the [MIT License](LICENSE).
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## Citation
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```bibtex
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@misc{jinnai2026cattranslatebuildingcompactopensource,
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title={CAT-Translate: Building Compact Open-Source Models for Japanese-English Translation},
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author={Yuu Jinnai},
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year={2026},
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eprint={2606.21413},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2606.21413},
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}
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```
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## Acknowledgments
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This project stands on the shoulders of giants. In particular, the following resources significantly helped us develop the model:
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- [Ryosuke Ishigami](https://huggingface.co/rishigami) for sharing the model
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- [sarashina](https://huggingface.co/sbintuitions) by SB Intuitions
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- [gpt-oss](https://huggingface.co/openai/gpt-oss-20b) by OpenAI
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- [MetricX](https://huggingface.co/google/metricx-24-hybrid-xl-v2p6-bfloat16) by Juraj Juraska et al.
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- [Duplodocus](https://github.com/allenai/duplodocus) by AllenAI
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- [fastText](https://github.com/facebookresearch/fastText) by Facebook Research
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- [COMET](https://huggingface.co/Unbabel/wmt22-comet-da) by Ricardo Rei et al.
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- [sacrebleu](https://github.com/mjpost/sacrebleu) by Matt Post
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- [Mitsuki Sakamoto](https://huggingface.co/Mitsuki-Sakamoto) for deploying the model with UI for internal testing
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chat_template.jinja
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{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = 'You are a helpful assistant.' %}{% endif %}{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in loop_messages %}{% if loop.index0 == 0 %}{{'<s><|im_start|>system
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' + system_message + '<|im_end|>
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'}}{% endif %}{{'<|im_start|>' + message['role'] + '
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' + message['content'] + '<|im_end|>' + '
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'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
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' }}{% endif %}
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config.json
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config.json
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{
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"dtype": "bfloat16",
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"eos_token_id": 61763,
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"head_dim": null,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pad_token_id": 61762,
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"rms_norm_eps": 1e-05,
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"rope_theta": 1000000.0,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"transformers_version": "4.57.1",
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"use_cache": false,
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"vocab_size": 61765
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}
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generation_config.json
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{
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"_from_model_config": true,
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"do_sample": true,
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"eos_token_id": [
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61763
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"transformers_version": "4.57.1"
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}
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||||
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||||
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||||
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||||
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|
||||
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||||
"model.layers.7.input_layernorm.weight": "model-00001-of-00004.safetensors",
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|
||||
"model.layers.7.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
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"model.layers.8.input_layernorm.weight": "model-00001-of-00004.safetensors",
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"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
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|
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"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
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|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00003-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
37
special_tokens_map.json
Normal file
37
special_tokens_map.json
Normal file
@@ -0,0 +1,37 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|padding|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"sep_token": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
20
test.py
Normal file
20
test.py
Normal file
@@ -0,0 +1,20 @@
|
||||
from transformers import pipeline
|
||||
|
||||
model_name = "CyberAgent/CAT-Translate-7b"
|
||||
chat_pipeline = pipeline("text-generation", model_name)
|
||||
|
||||
prompt = "Translate the following {src_lang} text into {tgt_lang}.\n\n {src_text}"
|
||||
|
||||
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)
|
||||
|
||||
print("-" * 20)
|
||||
print("Source Text:")
|
||||
print(src_text)
|
||||
print("Translation:")
|
||||
print(response[0]['generated_text'][-1]['content'])
|
||||
450208
tokenizer.json
Normal file
450208
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
70
tokenizer_config.json
Normal file
70
tokenizer_config.json
Normal file
@@ -0,0 +1,70 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"add_prefix_space": null,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"61762": {
|
||||
"content": "<|padding|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"61763": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"61764": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [],
|
||||
"bos_token": "<s>",
|
||||
"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = 'You are a helpful assistant.' %}{% endif %}{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in loop_messages %}{% if loop.index0 == 0 %}{{'<s><|im_start|>system\n' + system_message + '<|im_end|>\n'}}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"extra_special_tokens": {},
|
||||
"legacy": true,
|
||||
"model_max_length": 1000000000000000019884624838656,
|
||||
"pad_token": "<|padding|>",
|
||||
"sep_token": "<|im_start|>",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizerFast",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false
|
||||
}
|
||||
Reference in New Issue
Block a user