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---
language:
- en
- ja
library_name: transformers
pipeline_tag: text-generation
license: llama3
model_type: llama
---
# Llama3 Swallow
Our Swallow model has undergone continual pre-training from the [Llama 3 family](https://huggingface.co/collections/meta-llama/meta-llama-3-66214712577ca38149ebb2b6), primarily with the addition of Japanese language data. The Instruct versions use supervised fine-tuning (SFT) and Chat Vector. Links to other models can be found in the index.
# Model Release Updates
We are excited to share the release schedule for our latest models:
- **July 1, 2024**: Released the [Llama-3-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1), [Llama-3-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1), [Llama-3-Swallow-70B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-70B-v0.1), and [Llama-3-Swallow-70B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-70B-Instruct-v0.1).
## Swallow Model Index
|Model|Llama-3-Swallow|Llama3 Swallow Instruct|
|---|---|---|
|8B| [Link](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-8B-Instruct-v0.1) |
|70B| [Link](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-70B-v0.1) | [Link](https://huggingface.co/tokyotech-llm/Llama-3-Swallow-70B-Instruct-v0.1) |
This repository provides large language models developed by [Swallow-LLM](https://swallow-llm.github.io/).
Read our [blog post](https://zenn.dev/tokyotech_lm/articles/f65989d76baf2c).
## Model Details
* **Model type**: Please refer to [Llama 3 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 blog](https://ai.meta.com/blog/meta-llama-3/) for details on the tokenizer.
* **Contact**: swallow[at]nlp.c.titech.ac.jp
## Model Performance
### Japanese tasks
|Model|Size|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| |
|Llama-2-70b|70B|0.8651|0.5157|0.5464|0.9130|0.2372|0.3640|0.2657|0.2402|0.5496|0.2841|0.4781|
|Swallow-70b-hf|70B|0.9178|0.6178|**0.6910**|0.9208|0.2279|0.4720|0.3046|0.2301|0.5750|0.2262|0.5183|
|Qwen2-72B|72B|0.9607|0.6399|0.5617|**0.9261**|0.2362|**0.7560**|0.2747|0.2419|**0.7831**|**0.5567**|**0.5937**|
|Meta-Llama-3-70B|70B|0.9473|0.6042|0.5965|0.9207|0.2254|0.6720|0.2855|0.2526|0.6975|0.4799|0.5682|
|Llama-3-Swallow-70B-v0.1|70B|**0.9714**|**0.6695**|0.6881|0.9218|**0.2404**|0.7080|**0.3072**|**0.2548**|0.7049|0.4683|0.5934|
### English tasks
|Model|Size|OpenBookQA|TriviaQA|HellaSWAG|SQuAD2.0|XWINO|MMLU|GSM8K|BBH|HumanEval|En Avg|
|---|---|---|---|---|---|---|---|---|---|---|---|
| | |4-shot|4-shot|4-shot|4-shot|4-shot|5-shot|4-shot|3-shot|0-shot| |
| | |Acc|EM acc|Acc|EM acc|Acc|Acc|EM acc|CoT EM Acc|pass@1| |
|Llama-2-70b|70B|0.4260|0.7988|0.6681|0.3379|**0.9256**|0.6876|0.5466|0.6643|0.3152|0.5967|
|Swallow-70b-hf|70B|0.4160|0.7610|0.6433|0.3345|0.9191|0.6571|0.5080|0.6537|0.2409|0.5704|
|Qwen2-72B|72B|0.4160|0.7890|0.6766|0.4052|0.9161|**0.8428**|**0.8908**|0.6388|**0.6049**|0.6867|
|Meta-Llama-3-70B|70B|**0.4360**|**0.8263**|**0.6909**|**0.4071**|0.9213|0.7870|0.8014|**0.8266**|0.5177|**0.6905**|
|Llama-3-Swallow-70B-v0.1|70B|0.4240|0.8231|0.6828|0.4059|0.9234|0.7745|0.8143|0.7352|0.4909|0.6749|
## Evaluation Benchmarks
### 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])
- 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.
- [Algebraic Stack](https://huggingface.co/datasets/EleutherAI/proof-pile-2)
- [Cosmopedia](https://huggingface.co/datasets/HuggingFaceTB/cosmopedia)
- [English Wikipedia](https://dumps.wikimedia.org/other/cirrussearch)
- [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch)
- [Laboro ParaCorpus](https://github.com/laboroai/Laboro-ParaCorpus)
- [OpenWebMath](https://huggingface.co/datasets/EleutherAI/proof-pile-2)
- [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
- [Swallow Corpus](https://arxiv.org/abs/2404.17733)
## 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 under an open license for others to build on.
Our project is supported by the [Large Generative AI Development Support Program](https://abci.ai/en/link/lfm_support_program.html) of the National Institute of Advanced Industrial Science and Technology.
## License
[META LLAMA 3 COMMUNITY LICENSE](https://llama.meta.com/llama3/license/)
## Authors
Here are the team members:
- From [Tokyo Institute of Technology Okazaki Laboratory](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)
- [Koki Maeda](https://sites.google.com/view/silviase)
- [Kakeru Hattori](https://aya-se.vercel.app/)
- [Masanari Ohi](https://sites.google.com/view/masanariohi)
- [Taihei Shiotani](https://github.com/inatoihs)
- [Koshiro Saito](https://sites.google.com/view/koshiro-saito)
- From [Tokyo Institute of Technology YOKOTA Laboratory](https://www.rio.gsic.titech.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)
- 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 us.
```tex
@misc{llama3swallow,
title={Llama 3 Swallow},
url={https://swallow-llm.github.io/llama3-swallow.en.html},
author={Swallow LLM},
year={2024},
}
```
### Citations
```tex
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
``` |