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LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
Llama 3.1 Version Release Date: July 23, 2024
“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the
Llama Materials set forth herein.
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exclusive jurisdiction of any dispute arising out of this Agreement.

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Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.
Gemma is provided under and subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms

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---
license:
- llama3.3
- gemma
datasets:
- tokyotech-llm/swallow-code
- tokyotech-llm/swallow-math
language:
- en
- ja
base_model:
- meta-llama/Llama-3.1-8B-Instruct
model_type: llama
---
# Llama 3.1 Swallow v0.5 - Built with Llama
Llama 3.1 Swallow v0.5 is a large language model (8B) that was built by continual pre-training on the [Meta Llama 3.1](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) model.
Llama 3.1 Swallow v0.5 enhanced the Japanese language and reasoning(code & math) capabilities of the original Llama 3.1 while retaining the English language capabilities.
We use approximately 210 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 (Instruct) 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.
# Release History
- **Jun 25, 2025**: Released [Llama-3.1-Swallow-8B-Instruct-v0.5](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5) and [Llama-3.1-Swallow-8B-v0.5](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.5).
- **March 10, 2025**: Released [Llama-3.3-Swallow-70B-Instruct-v0.4](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4) and [Llama-3.3-Swallow-70B-v0.4](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-v0.4).
- **December 30, 2024**: Released [Llama-3.1-Swallow-70B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3).
- **December 23, 2024**: Released [Llama-3.1-Swallow-8B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3).
- **November 11, 2024**: Released [Llama-3.1-Swallow-8B-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2) and [Llama-3.1-Swallow-8B-Instruct-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2).
- **October 08, 2024**: Released [Llama-3.1-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1), [Llama-3.1-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1), [Llama-3.1-Swallow-70B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1), and [Llama-3.1-Swallow-70B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1).
## Swallow Model Index
|Model|Llama-3.1-Swallow-Instruct v0.5|Llama-3.1-Swallow v0.5|Llama-3.3-Swallow v0.4|Llama-3.3-Swallow-Instruct v0.4|Llama-3.1-Swallow-Instruct v0.3|Llama-3.1-Swallow-Instruct v0.2|Llama-3.1-Swallow v0.2|Llama-3.1-Swallow-Instruct v0.1|Llama-3.1-Swallow v0.1|
|---|---|---|---|---|---|---|---|---|---|
|8B|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.5) |||[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1)|
|70B|||[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-v0.4)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3)| | |[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1)| [🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1)|
![logo](./logo.png)
The website [https://swallow-llm.github.io/](https://swallow-llm.github.io/index.en.html) provides large language models developed by the Swallow team.
## Model Details
* **Model type**: Please refer to [Llama 3.1 MODEL_CARD](https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md) for details on the model architecture.
* **Language(s)**: Japanese English
* **Library**: [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
* **Tokenizer**: Please refer to [Llama 3.1 blog](https://ai.meta.com/blog/meta-llama-3-1) for details on the tokenizer.
* **Contact**: swallow[at]nlp.c.titech.ac.jp
## Model Performance
### 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 | |
| Qwen2.5-7B | 0.924 | 0.459 | 0.426 | 0.907 | 0.216 | 0.616 | 0.229 | 0.199 | 0.634 | 0.507 | 0.512 |
| Llama 3.1 8B | 0.845 | 0.461 | 0.405 | 0.895 | 0.179 | 0.356 | 0.221 | 0.210 | 0.479 | 0.320 | 0.437 |
| Qwen3-8B-Base | 0.927 | **0.537** | 0.475 | 0.912 | 0.207 | **0.716** | 0.241 | 0.215 | **0.689** | **0.595** | **0.551** |
| Llama 3.1 Swallow 8B v0.2 | 0.911 | 0.510 | 0.627 | 0.892 | 0.198 | 0.464 | **0.296** | **0.233** | 0.525 | 0.336 | 0.499 |
| **Llama 3.1 Swallow 8B v0.5** | **0.952** | 0.513 | **0.657** | **0.910** | **0.217** | 0.572 | 0.294 | 0.232 | 0.590 | 0.491 | 0.543 |
### 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 | |
| Qwen2.5-7B | **0.392** | 0.601 | 0.600 | **0.618** | 0.888 | 0.742 | 0.832 | 0.510 | 0.562 | 0.554 | 0.630 |
| Qwen3-8B-Base | 0.382 | 0.618 | 0.594 | 0.602 | 0.903 | **0.765** | **0.855** | **0.622** | **0.655** | **0.669** | **0.667** |
| Llama 3.1 8B | 0.380 | **0.702** | **0.609** | 0.503 | **0.907** | 0.651 | 0.507 | 0.214 | 0.616 | 0.364 | 0.545 |
| Llama 3.1 Swallow 8B v0.2 | 0.382 | 0.651 | 0.596 | 0.513 | 0.904 | 0.622 | 0.521 | 0.228 | 0.605 | 0.366 | 0.539 |
| **Llama 3.1 Swallow 8B v0.5** | 0.372 | 0.665 | 0.597 | 0.536 | 0.900 | 0.666 | 0.699 | 0.390 | 0.589 | 0.557 | 0.597 |
## Evaluation Benchmarks
The evaluation script can be found at [swallow-llm/swallow-evaluation](https://github.com/swallow-llm/swallow-evaluation), tagged as `v202411`.
### 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])
## Training Datasets
### Continual Pre-Training
The following datasets were used for continual pre-training.
- [Cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
- [Dclm-baseline-1.0](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0)
- [English Wikipedia](https://dumps.wikimedia.org/other/cirrussearch)
- [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch)
- [Laboro ParaCorpus](https://github.com/laboroai/Laboro-ParaCorpus)
- [Swallow Corpus Version 2](https://arxiv.org/abs/2404.17733) (filtered using [Swallow Education Classifier(Wiki-based)](https://huggingface.co/tokyotech-llm/edu-classifier))
- [Swallow Corpus Version 2](https://arxiv.org/abs/2404.17733) (filtered using [Swallow Education Classifier](https://huggingface.co/tokyotech-llm/edu-classifier))
- [Swallow Corpus Version 2](https://arxiv.org/abs/2404.17733) (synthetic QA-format using Gemma-2-27b-it)
- [Swallow Code Version 1](https://huggingface.co/datasets/tokyotech-llm/swallow-code)
- [Swallow Math Version 1](https://huggingface.co/datasets/tokyotech-llm/swallow-math)
### Swallow Corpus Version 2
We built the Swallow Corpus by extracting high-quality Japanese texts from Common Crawl. In Version 2, we expanded the scope of the Common Crawl collection and modified the pipeline sequence to enable more flexible quality filtering.
For Llama 3.1 Swallow v0.2, we further refined our quality filtering and data sampling strategies, resulting in an even higher-quality selection of Japanese texts for pre-training.
For Llama 3.3 Swallow 70B v0.4, we generated synthetic QA-format text by using Gemma 2 27B IT to paraphrase educational web documents from our corpus.
### Swallow Code & Swallow Math
Swallow Code and Swallow Math are high-quality, open-source datasets developed and publicly released by our team at the Institute of Science Tokyo, in collaboration with the Artificial Intelligence Research Center, AIST, Japan.
These datasets are specifically designed to enhance the code and mathematical reasoning capabilities of large language models, with a focus on improving performance in Japanese and English tasks.
As demonstrated in our paper, ["Rewriting Pre-Training Data Boosts LLM Performance in Math and Code"](https://arxiv.org/abs/2505.02881), Swallow Code and Swallow Math outperform other datasets such as [Stack-Edu](https://huggingface.co/datasets/HuggingFaceTB/stack-edu) and [finemath-4+](https://huggingface.co/datasets/HuggingFaceTB/finemath) in terms of quality and effectiveness.
## 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 Meta Research for releasing Llama 3.3 under a generous open license.
We would like to thank Amazon Web Services (AWS) for providing access to [SageMaker HyperPod](https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-hyperpod.html), which enabled the training of the Llama 3.1 Swallow project.
We received various supports 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](https://abci.ai/en/link/lfm_support_program.html)
## License
[META LLAMA 3.3 COMMUNITY LICENSE](https://www.llama.com/llama3_3/license/) and [Gemma Terms of Use](https://ai.google.dev/gemma/terms)
## Authors
Here are the team members:
- From [Okazaki Laboratory, Institute of Science Tokyo](https://www.nlp.c.titech.ac.jp/index.en.html), the following members:
- [Naoaki Okazaki](https://www.chokkan.org/index.ja.html)
- [Sakae Mizuki](https://s-mizuki-nlp.github.io/)
- [Youmi Ma](https://www.nlp.c.titech.ac.jp/member/youmi.en.html)
- [Sangwhan Moon](https://www.sangwhan.com/)
- [Koki Maeda](https://sites.google.com/view/silviase)
- [Masanari Ohi](https://sites.google.com/view/masanariohi)
- [Hinari Shimada](https://hinarishimada.github.io/portfolio)
- [Taihei Shiotani](https://github.com/inatoihs)
- [Koshiro Saito](https://sites.google.com/view/koshiro-saito)
- [Tatsuya Ichinose](https://tatsuya736482.github.io/myprofile)
- Naoya Matsushita
- Sora Miyamoto
- Nguyen Tien Dung
- Yuta Katayama
- From [YOKOTA Laboratory, Institute of Science Tokyo](https://www.rio.scrc.iir.isct.ac.jp/en/index.html), the following members:
- [Rio Yokota](https://twitter.com/rioyokota)
- [Kazuki Fujii](https://twitter.com/okoge_kaz)
- [Taishi Nakamura](https://twitter.com/Setuna7777_2)
- [Takumi Okamoto](https://www.linkedin.com/in/takumi-okamoto)
- [Ishida Shigeki](https://www.wantedly.com/id/reborn27)
- Masaki Kawamura
- Yukito Tajima
- From [Artificial Intelligence Research Center, AIST, Japan](https://www.airc.aist.go.jp/en/teams/), the following members:
- [Hiroya Takamura](https://sites.google.com/view/hjtakamura)
## How to cite
If you find our work 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{fujii2025rewritingpretrainingdataboosts,
title={Rewriting Pre-Training Data Boosts LLM Performance in Math and Code},
author={Kazuki Fujii and Yukito Tajima and Sakae Mizuki and Hinari Shimada and Taihei Shiotani and Koshiro Saito and Masanari Ohi and Masaki Kawamura and Taishi Nakamura and Takumi Okamoto and Shigeki Ishida and Kakeru Hattori and Youmi Ma and Hiroya Takamura and Rio Yokota and Naoaki Okazaki},
year={2025},
eprint={2505.02881},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2505.02881},
}
```
### References
```tex
@misc{dubey2024llama3herdmodels,
title={The Llama 3 Herd of Models},
author={Abhimanyu Dubey and Abhinav Jauhri and Abhinav Pandey and Abhishek Kadian and Ahmad Al-Dahle and Aiesha Letman and Akhil Mathur and Alan Schelten and Amy Yang and Angela Fan et al.},
year={2024},
eprint={2407.21783},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2407.21783},
}
```

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# Llama 3.1 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.1. If you
access or use Llama 3.1, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of
this policy can be found at [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)
## Prohibited Uses
We want everyone to use Llama 3.1 safely and responsibly. You agree you will not use, or allow
others to use, Llama 3.1 to:
1. Violate the law or others’ rights, including to:
1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
1. Violence or terrorism
2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
3. Human trafficking, exploitation, and sexual violence
4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
5. Sexual solicitation
6. Any other criminal activity
3. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
4. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
5. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
6. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws
7. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials
8. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.1 related to the following:
1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State
2. Guns and illegal weapons (including weapon development)
3. Illegal drugs and regulated/controlled substances
4. Operation of critical infrastructure, transportation technologies, or heavy machinery
5. Self-harm or harm to others, including suicide, cutting, and eating disorders
6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Llama 3.1 related to the following:
1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
3. Generating, promoting, or further distributing spam
4. Impersonating another individual without consent, authorization, or legal right
5. Representing that the use of Llama 3.1 or outputs are human-generated
6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI system
Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation
of this Policy through one of the following means:
* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)
* Reporting risky content generated by the model: developers.facebook.com/llama_output_feedback
* Reporting bugs and security concerns: facebook.com/whitehat/info
* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.1: LlamaUseReport@meta.com

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