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Meta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.

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---
language:
- en
- ja
library_name: transformers
pipeline_tag: text-generation
license: llama3
model_type: llama
---
# Llama3 Swallow - Built with Meta Llama 3
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) |
![logo](./logo.png)
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| |
|calm2-7b-chat|7B|0.2413|0.5128|0.4956|0.7729|0.0551|0.0480|0.2208|0.1384|0.2482|0.0000|0.2733|
|Swallow-7b-instruct-v0.1|7B|0.6059|0.4760|0.5284|0.8396|0.1546|0.1360|0.2285|0.1783|0.3510|0.0256|0.3524|
|Swallow-MS-7b-instruct-v0.1|7B|0.7435|0.5066|0.4268|0.8594|0.1582|0.1760|0.2260|0.1880|0.4177|0.2244|0.3927|
|RakutenAI-7B-chat|7B|0.9035|0.2600|0.4619|0.8647|0.1339|0.2120|0.2667|0.1966|0.4504|0.2299|0.3980|
|Qwen2-7B-Instruct|7B|0.8856|0.3902|0.3859|0.8967|0.1277|0.5720|0.2041|0.1909|0.5713|0.5683|0.4793|
|Meta-Llama-3-8B-Instruct|8B|0.8785|0.3812|0.3936|0.8955|0.1273|0.4160|0.2143|0.2035|0.4719|0.2872|0.4269|
|Llama-3-ELYZA-JP-8B|8B|0.9017|0.5124|0.5016|0.9113|0.1677|0.4600|0.2509|0.1846|0.4829|0.3811|0.4754|
|Llama-3-Swallow-8B-Instruct-v0.1|8B|0.9178|0.4963|0.5168|0.9088|0.1296|0.4880|0.2522|0.2254|0.4835|0.3927|0.4811|
### 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| |
|calm2-7b-chat|7B|0.2860|0.3528|0.5042|0.2524|0.8413|0.3860|0.0546|0.2990|0.0000|0.3307|
|Swallow-7b-instruct-v0.1|7B|0.3280|0.4810|0.5501|0.2720|0.8774|0.4066|0.1251|0.3646|0.0866|0.3879|
|Swallow-MS-7b-instruct-v0.1|7B|0.3600|0.4999|0.5858|0.3030|0.8834|0.5273|0.2108|0.4386|0.2512|0.4511|
|RakutenAI-7B-chat|7B|0.4160|0.5971|0.6465|0.3091|0.8886|0.5757|0.3139|0.4958|0.2671|0.5011|
|Qwen2-7B-Instruct|7B|0.4000|0.5468|0.6146|0.3518|0.8852|0.7073|0.6300|0.3101|0.6354|0.5646|
|Meta-Llama-3-8B-Instruct|8B|0.3880|0.6687|0.5834|0.3743|0.8903|0.6567|0.7453|0.6478|0.5415|0.6107|
|Llama-3-ELYZA-JP-8B|8B|0.3200|0.5502|0.5224|0.3631|0.8809|0.5875|0.5701|0.3213|0.4604|0.5084|
|Llama-3-Swallow-8B-Instruct-v0.1|8B|0.3720|0.6557|0.5861|0.3648|0.9002|0.6315|0.5959|0.6391|0.4238|0.5743|
## MT-Bench JA
|Model|Size|coding|extraction|humanities|math|reasoning|roleplay|stem|writing|JMTAvg|
|---|---|---|---|---|---|---|---|---|---|---|
|calm2-7b-chat|7B|0.1198|0.3793|0.4231|0.1011|0.1799|0.4760|0.3568|0.4583|0.3118|
|Swallow-7b-instruct-v0.1|7B|0.1947|0.3156|0.4991|0.1900|0.2141|0.5330|0.4535|0.4624|0.3578|
|Swallow-MS-7b-instruct-v0.1|7B|0.2235|0.3743|0.4611|0.1060|0.3404|0.4287|0.3969|0.3877|0.3398|
|RakutenAI-7B-chat|7B|0.2475|0.3522|0.4692|0.2140|0.3926|0.4427|0.3977|0.4434|0.3699|
|Qwen2-7B-Instruct|7B|0.4635|0.6909|0.6857|0.5970|0.5042|0.6667|0.5353|0.6808|0.6030|
|Meta-Llama-3-8B-Instruct|8B|0.3744|0.6876|0.6225|0.2070|0.5032|0.5248|0.5326|0.4884|0.4926|
|Llama-3-ELYZA-JP-8B|8B|0.2908|0.6421|0.6406|0.3088|0.5500|0.6740|0.5251|0.6744|0.5382|
|Llama-3-Swallow-8B-Instruct-v0.1|8B|0.3547|0.6508|0.5371|0.2718|0.4007|0.5493|0.4752|0.5730|0.4766|
## 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])
### MT-Bench JA
We used [Japanese MT-Bench](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question) to assess the instruction-following capabilities of models.
We utilized the following settings:
- Implemantation: FastChat [Zheng+, 2023] (commit #e86e70d0)
- Question: [Nejumi LLM-Leaderboard NEO, mtbench_ja_question_v3](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question/v3)
- Reference Answer: [Nejumi LLM-Leaderboard NEO, mtbench_ja_referenceanswer_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_referenceanswer/v1)
- Prompt for Judge: [Nejumi LLM-Lederboard NEO, mtbench_ja_prompt_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_prompt/v1)
- Judge: `gpt-4-1106-preview`
- Scoring: Absolute scale normalized to a 0-1 range, averaged over five runs.
## Usage
```sh
pip install vllm
```
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
model_name = "tokyotech-llm/Llama-3-Swallow-8B-Instruct-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, stop="<|eot_id|>"
)
message = [
{"role": "system", "content": "あなたは誠実で優秀な日本人のアシスタントです。"},
{
"role": "user",
"content": "東京の夜空に打ち上がっている花火の下、向かい合っている燕とラマの温かい物語を書いてください。",
},
]
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.
- [OpenAssistant Conversations Dataset EN top-1 thread](https://huggingface.co/datasets/OpenAssistant/oasst2)
- [OpenAssistant Conversations Dataset](https://huggingface.co/datasets/llm-jp/oasst1-21k-ja) was used, where human utterances are included but the responses are not used. Instead, the responses were generated using the [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) model.
## 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.
```
@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},
}
```
### 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}
}
```

53
USE_POLICY.md Normal file
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# Meta Llama 3 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you
access or use Meta Llama 3, you agree to this Acceptable Use Policy (“Policy”). The most recent copy of
this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)
## Prohibited Uses
We want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow
others to use, Meta Llama 3 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
2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
3. 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
4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
5. 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
6. 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
7. 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 Meta Llama 3 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 Meta Llama 3 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 Meta Llama 3 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/llama3](https://github.com/meta-llama/llama3)
● 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 Meta Llama 3:
LlamaUseReport@meta.com

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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

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