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Gemma-2-Llama-Swallow-2b-it-v0.1

Text Generation
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gemma2
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Gemma-2-Llama-Swallow

Gemma-2-Llama-Swallow series was built by continual pre-training on the gemma-2 models. Gemma 2 Swallow enhanced the Japanese language capabilities of the original Gemma 2 while retaining the English language capabilities. We use approximately 200 billion tokens that were sampled from a large Japanese web corpus (Swallow Corpus Version 2), Japanese and English Wikipedia articles, and mathematical and coding contents, etc (see the Training Datasets section of the base model) for continual pre-training. The instruction-tuned models (it) were built by supervised fine-tuning (SFT) on the synthetic data specially built for Japanese. See the Swallow Model Index section to find other model variants. Built with Gemma. Built with Llama.

Release History

Swallow Model Index

Model gemma-2-swallow v0.1 gemma-2-swallow-it v0.1
2B 🤗 HuggingFace 🤗 HuggingFace
9B 🤗 HuggingFace 🤗 HuggingFace
27B 🤗 HuggingFace 🤗 HuggingFace

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The website https://swallow-llm.github.io/ provides large language models developed by the Swallow team.

Model Details

  • Model type: Please refer to Gemma 2 paper for details on the model architecture.
  • Language(s): Japanese, English
  • Library: maxtext
  • Tokenizer: Please refer to Gemma 2 paper for details on the tokenizer.
  • Contact: swallow[at]nlp.c.titech.ac.jp

Model Performance

MT-Bench JA

Model coding extraction humanities math reasoning roleplay stem writing JMT Avg
google/gemma-3-1b-it 0.379 0.497 0.680 0.385 0.322 0.628 0.540 0.651 0.510
Qwen/Qwen2.5-1.5B-Instruct 0.408 0.513 0.456 0.527 0.352 0.473 0.406 0.469 0.450
google/gemma-2-2b-it 0.454 0.587 0.693 0.524 0.445 0.654 0.567 0.630 0.569
rinna/gemma-2-baku-2b-it 0.470 0.625 0.810 0.414 0.382 0.713 0.609 0.697 0.590
google/gemma-2-2b-jpn-it 0.467 0.488 0.741 0.379 0.406 0.660 0.589 0.672 0.550
tokyotech-llm/Gemma-2-Llama-Swallow-2b-it-v0.1 0.438 0.533 0.781 0.557 0.404 0.706 0.674 0.682 0.597
Qwen/Qwen2.5-3B-Instruct 0.567 0.647 0.597 0.665 0.457 0.649 0.526 0.637 0.593
google/gemma-3-4b-it 0.603 0.724 0.798 0.767 0.498 0.803 0.775 0.822 0.724
Qwen/Qwen2.5-7B-Instruct 0.599 0.741 0.719 0.637 0.541 0.744 0.624 0.713 0.665
tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3 0.562 0.756 0.869 0.610 0.512 0.783 0.748 0.803 0.705
google/gemma-2-9b-it 0.652 0.765 0.857 0.614 0.673 0.811 0.713 0.800 0.736
tokyotech-llm/Gemma-2-Llama-Swallow-9b-it-v0.1 0.592 0.796 0.872 0.742 0.638 0.802 0.745 0.803 0.749
google/gemma-3-12b-it 0.807 0.814 0.871 0.886 0.623 0.847 0.858 0.863 0.821
google/gemma-2-27b-it 0.727 0.809 0.874 0.719 0.639 0.810 0.740 0.826 0.768
tokyotech-llm/Gemma-2-Llama-Swallow-27b-it-v0.1 0.618 0.839 0.873 0.741 0.608 0.814 0.739 0.836 0.759
google/gemma-3-27b-it 0.804 0.927 0.879 0.876 0.774 0.846 0.848 0.882 0.855
Qwen/Qwen2.5-32B-Instruct 0.724 0.885 0.816 0.918 0.726 0.834 0.763 0.808 0.809

Japanese tasks

Model JCom. JEMHopQA NIILC JSQuAD XL-Sum MGSM WMT20-en-ja WMT20-ja-en JMMLU JHumanEval Ja Avg
4-shot 4-shot 4-shot 4-shot 1-shot 4-shot 4-shot 4-shot 5-shot 0-shot
EM acc Char-F1 Char-F1 Char-F1 ROUGE-2 EM acc BLEU BLEU EM acc pass@1
google/gemma-3-1b-it 0.526 0.330 0.237 0.700 0.113 0.088 0.166 0.115 0.332 0.245 0.285
Qwen/Qwen2.5-1.5B-Instruct 0.812 0.276 0.241 0.847 0.128 0.292 0.147 0.119 0.447 0.242 0.355
google/gemma-2-2b-it 0.862 0.348 0.315 0.879 0.117 0.252 0.207 0.183 0.437 0.321 0.392
rinna/gemma-2-baku-2b-it 0.855 0.228 0.390 0.877 0.115 0.172 0.255 0.190 0.415 0.165 0.366
google/gemma-2-2b-jpn-it 0.845 0.321 0.291 0.877 0.131 0.192 0.204 0.180 0.418 0.311 0.377
tokyotech-llm/Gemma-2-Llama-Swallow-2b-it-v0.1 0.862 0.367 0.483 0.881 0.145 0.288 0.258 0.200 0.485 0.267 0.424
Qwen/Qwen2.5-3B-Instruct 0.876 0.304 0.293 0.866 0.144 0.228 0.198 0.168 0.536 0.474 0.409
google/gemma-3-4b-it 0.818 0.444 0.404 0.801 0.134 0.332 0.217 0.169 0.477 0.365 0.416
Qwen/Qwen2.5-7B-Instruct 0.915 0.429 0.391 0.891 0.168 0.632 0.211 0.192 0.623 0.532 0.498
tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3 0.924 0.528 0.583 0.896 0.191 0.532 0.281 0.229 0.544 0.394 0.510
google/gemma-2-9b-it 0.931 0.532 0.527 0.876 0.149 0.636 0.273 0.239 0.623 0.559 0.535
tokyotech-llm/Gemma-2-Llama-Swallow-9b-it-v0.1 0.946 0.606 0.643 0.852 0.170 0.624 0.296 0.238 0.639 0.446 0.546
google/gemma-3-12b-it 0.935 0.566 0.542 0.808 0.148 0.724 0.289 0.239 0.645 0.637 0.553
google/gemma-2-27b-it 0.956 0.541 0.576 0.883 0.166 0.704 0.290 0.249 0.670 0.638 0.567
tokyotech-llm/Gemma-2-Llama-Swallow-27b-it-v0.1 0.969 0.654 0.658 0.891 0.194 0.764 0.316 0.258 0.686 0.635 0.602
google/gemma-3-27b-it 0.946 0.592 0.584 0.867 0.142 0.764 0.307 0.253 0.716 0.736 0.591
Qwen/Qwen2.5-32B-Instruct 0.959 0.567 0.497 0.903 0.169 0.780 0.228 0.195 0.757 0.651 0.571

English tasks

Model OpenBookQA TriviaQA HellaSWAG SQuAD2.0 XWINO MMLU GSM8K MATH BBH HumanEval En Avg
4-shot 4-shot 4-shot 4-shot 4-shot 5-shot 4-shot 4-shot 3-shot 0-shot
Acc EM acc Acc EM acc Acc Acc EM acc CoT EM Acc CoT EM Acc pass@1
google/gemma-3-1b-it 0.272 0.229 0.421 0.501 0.786 0.398 0.256 0.340 0.379 0.335 0.392
Qwen/Qwen2.5-1.5B-Instruct 0.334 0.378 0.503 0.501 0.844 0.604 0.257 0.272 0.272 0.277 0.424
google/gemma-2-2b-it 0.354 0.502 0.520 0.548 0.878 0.569 0.440 0.230 0.464 0.382 0.489
rinna/gemma-2-baku-2b-it 0.342 0.416 0.511 0.522 0.871 0.526 0.027 0.174 0.063 0.158 0.361
google/gemma-2-2b-jpn-it 0.370 0.503 0.532 0.539 0.879 0.557 0.351 0.132 0.451 0.392 0.471
tokyotech-llm/Gemma-2-Llama-Swallow-2b-it-v0.1 0.332 0.417 0.529 0.506 0.856 0.530 0.284 0.150 0.405 0.301 0.431
Qwen/Qwen2.5-3B-Instruct 0.364 0.446 0.562 0.504 0.869 0.664 0.096 0.612 0.128 0.471 0.472
google/gemma-3-4b-it 0.412 0.500 0.560 0.552 0.872 0.583 0.769 0.306 0.598 0.513 0.566
Qwen/Qwen2.5-7B-Instruct 0.428 0.519 0.624 0.569 0.877 0.742 0.739 0.688 0.217 0.636 0.604
tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3 0.396 0.629 0.593 0.570 0.884 0.629 0.622 0.266 0.626 0.445 0.566
google/gemma-2-9b-it 0.432 0.658 0.605 0.659 0.904 0.723 0.779 0.394 0.719 0.613 0.649
tokyotech-llm/Gemma-2-Llama-Swallow-9b-it-v0.1 0.404 0.640 0.609 0.623 0.900 0.680 0.710 0.392 0.663 0.491 0.611
google/gemma-3-12b-it 0.422 0.665 0.639 0.649 0.901 0.721 0.867 0.796 0.802 0.712 0.717
google/gemma-2-27b-it 0.458 0.766 0.655 0.669 0.909 0.762 0.851 0.466 0.790 0.707 0.703
tokyotech-llm/Gemma-2-Llama-Swallow-27b-it-v0.1 0.424 0.747 0.663 0.664 0.911 0.749 0.821 0.442 0.772 0.682 0.687
google/gemma-3-27b-it 0.418 0.744 0.661 0.687 0.906 0.774 0.916 0.852 0.793 0.829 0.758
Qwen/Qwen2.5-32B-Instruct 0.424 0.534 0.671 0.536 0.893 0.834 0.581 0.802 0.017 0.589 0.588

Evaluation Benchmarks

The evaluation script can be found at swallow-llm/swallow-evaluation, tagged as v202411.

MT-Bench JA

We used Japanese MT-Bench to assess the capabilities of multi-turn dialogue with the following settings:

Japanese evaluation benchmarks

We used llm-jp-eval(v1.3.0), JP Language Model Evaluation Harness(commit #9b42d41) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:

  • Multiple-choice question answering (JCommonsenseQA [Kurihara et al., 2022])
  • Open-ended question answering (JEMHopQA [Ishii et al., 2024])
  • Open-ended question answering (NIILC [関根, 2003])
  • Machine reading comprehension (JSQuAD [Kurihara et al., 2022])
  • Automatic summarization (XL-Sum [Hasan et al., 2021])
  • Machine translation (WMT2020 ja-en [Barrault et al., 2020])
  • Machine translation (WMT2020 en-ja [Barrault et al., 2020])
  • Mathematical reasoning (MGSM [Shi et al., 2023])
  • Academic exams (JMMLU [尹ら, 2024])
  • Code generation (JHumanEval [佐藤ら, 2024])

English evaluation benchmarks

We used the Language Model Evaluation Harness(v.0.4.2) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:

  • Multiple-choice question answering (OpenBookQA [Mihaylov et al., 2018])
  • Open-ended question answering (TriviaQA [Joshi et al., 2017])
  • Machine reading comprehension (SQuAD2 [Rajpurkar et al., 2018])
  • Commonsense reasoning (XWINO [Tikhonov and Ryabinin, 2021])
  • Natural language inference (HellaSwag [Zellers et al., 2019])
  • Mathematical reasoning (GSM8K [Cobbe et al., 2021])
  • Mathematical reasoning (MATH [Hendrycks et al., 2022][Lightman et al., 2024])
  • Reasoning (BBH (BIG-Bench-Hard) [Suzgun et al., 2023])
  • Academic exams (MMLU [Hendrycks et al., 2021])
  • Code generation (HumanEval [Chen et al., 2021])

Usage 

pip install vllm

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

model_name = "tokyotech-llm/Gemma-2-Llama-Swallow-27b-it-v0.1"

tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(
    model=model_name,
    tensor_parallel_size=1,
)

sampling_params = SamplingParams(
    temperature=0.6, top_p=0.9, max_tokens=512,
)


message = [
    {
        "role": "user",
        "content": "日本の春から夏の移り変わりについて教えてください",
    },
]
prompt = tokenizer.apply_chat_template(
    message, tokenize=False, add_generation_prompt=True
)

output = llm.generate(prompt, sampling_params)

print(output[0].outputs[0].text)

Training Datasets

Instruction Tuning

The following datasets were used for the instruction tuning.

  • Gemma-2-LMSYS-Chat-1M-Synth
    • Multi-turn Japanese instruction dataset synthesized and derived from lmsys-chat-1m [Zhang+, ICLR24]).
    • First-turn user instructions were translated into Japanese via DeepL (machine translation), and assistant responses were generated using gemma-2-27b-it. The same model, i.e., gemma-2-27b-it served as a judge for rejection sampling (n=6).
    • Second-turn user instructions and responses were synthesized using gemma-2-27b-it. The same model scores the quality of the second-turn response with a range of 1-10. Second-turn responses with scores lower than 9 were rejected, along with their corresponding instructions.
      Conversations containing personally identifiable information (PII) and template-based user instructions were removed. Duplicate instructions were removed.
  • Swallow-Magpie-Ultra-v0.1
    • A Japanese variant of the filtered-magpie-ultra-en dataset, translated into Japanese by gemma-2-27b-it.
  • Swallow-Gemma-Magpie-v0.1
    • A Japanese synthetic instruction tuning dataset from scratch, generated by gemma-2-27b-it. User instructions were created with prompts specific to each topic, and assistant responses were generated for these instructions.
    • The conversations were heuristically filtered for quality and length. Then, gemma-2-27b-it was applied to score the quality of each of the conversation with a range of 1-10. Conversations with scores <= 7 were rejected.

Risks and Limitations

The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.

Acknowledgements

We thank Google DeepMind for releasing Gemma 2 under a generous open license.

We received various support, including:

  • AIST project: "Research and Development of Foundation Models for Generative AI in the Physical Domain"
  • NEDO project: "Development of Artificial Intelligence Application Technology to Support Judgment in Design Risk Assessment Work Based on the Perspective of Skilled Persons" (JPNP18002) of "Development of Integration Technology as the Core of Next Generation Artificial Intelligence and Robotics"
  • MEXT project: "Formation of R&D center to ensure transparency and reliability of generative AI models"
  • AIST program: Large Generative AI Development Support Program
  • TPU Research Cloud

License

Gemma Terms of Use and META LLAMA 3.3 COMMUNITY LICENSE

Authors

Team members:

How to cite

If you find our work is helpful, please feel free to cite these papers.

@inproceedings{Fujii:COLM2024,
   title={Continual Pre-Training for Cross-Lingual LLM Adaptation:
Enhancing Japanese Language Capabilities},
   author={Kazuki Fujii and Taishi Nakamura and Mengsay Loem and Hiroki
Iida and Masanari Ohi and Kakeru Hattori and Hirai Shota and Sakae
Mizuki and Rio Yokota and Naoaki Okazaki},
   booktitle="Proceedings of the First Conference on Language Modeling",
   series={COLM},
   pages="(to appear)",
   year="2024",
   month=oct,
   address={University of Pennsylvania, USA},
}

@inproceedings{Okazaki:COLM2024,
   title={Building a Large Japanese Web Corpus for Large Language Models},
   author={Naoaki Okazaki and Kakeru Hattori and Hirai Shota and Hiroki
Iida and Masanari Ohi and Kazuki Fujii and Taishi Nakamura and Mengsay
Loem and Rio Yokota and Sakae Mizuki},
   booktitle="Proceedings of the First Conference on Language Modeling",
   series={COLM},
   pages="(to appear)",
   year="2024",
   month=oct,
   address={University of Pennsylvania, USA},
}

@misc{ma:arxiv2025,
      title={Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models},
      author={Youmi Ma and Sakae Mizuki and Kazuki Fujii and Taishi Nakamura and Masanari Ohi and Hinari Shimada and Taihei Shiotani and Koshiro Saito and Koki Maeda and Kakeru Hattori and Takumi Okamoto and Shigeki Ishida and Rio Yokota and Hiroya Takamura and Naoaki Okazaki},
      year={2025},
      eprint={2503.23714},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.23714},
}

References 

@misc{gemmateam2024gemma2improvingopen,
      title={Gemma 2: Improving Open Language Models at a Practical Size},
      author={Gemma Team},
      year={2024},
      eprint={2408.00118},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2408.00118},
}
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