Llama3 Swallow
Our Swallow model has undergone continual pre-training from the Llama 3 family, 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, Llama-3-Swallow-8B-Instruct-v0.1, Llama-3-Swallow-70B-v0.1, and Llama-3-Swallow-70B-Instruct-v0.1.
Swallow Model Index
This repository provides large language models developed by Swallow-LLM. Read our blog post.
Model Details
- Model type: Please refer to Llama 3 MODEL_CARD for details on the model architecture.
- Language(s): Japanese English
- Library: Megatron-LM
- Tokenizer: Please refer to Llama 3 blog 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 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
- Reference Answer: Nejumi LLM-Leaderboard NEO, mtbench_ja_referenceanswer_v1
- Prompt for Judge: Nejumi LLM-Lederboard NEO, mtbench_ja_prompt_v1
- Judge:
gpt-4-1106-preview
- Scoring: Absolute scale normalized to a 0-1 range, averaged over five runs.
Usage
pip install vllm
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
- OpenAssistant Conversations Dataset 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 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 of the National Institute of Advanced Industrial Science and Technology.
License
META LLAMA 3 COMMUNITY LICENSE
Authors
Here are the team members:
- From Tokyo Institute of Technology Okazaki Laboratory, the following members:
- From Tokyo Institute of Technology YOKOTA Laboratory, the following members:
- From Artificial Intelligence Research Center, AIST, Japan, the following members:
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
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
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