tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1

Llama 3.1 Swallow - Built with Llama

Llama 3.1 Swallow is a series of large language models (8B, 70B) that were built by continual pre-training on the Meta Llama 3.1 models. Llama 3.1 Swallow enhanced the Japanese language capabilities of the original Llama 3.1 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 (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

• October 08, 2024: Released Llama-3.1-Swallow-8B-v0.1, Llama-3.1-Swallow-8B-Instruct-v0.1, Llama-3.1-Swallow-70B-v0.1, and Llama-3.1-Swallow-70B-Instruct-v0.1.

Swallow Model Index

Model	Llama-3.1-Swallow	Llama-3.1-Swallow-Instruct
8B	Link	Link
70B	Link	Link

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

Model Details

• Model type: Please refer to Llama 3.1 MODEL_CARD for details on the model architecture.
• Language(s): Japanese English
• Library: Megatron-LM
• Tokenizer: Please refer to Llama 3.1 blog 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
Qwen2-72B-Instruct	0.9634	0.6268	0.5418	0.9210	0.1644	0.7840	0.2592	0.2327	0.7713	0.6909	0.5955
Qwen2.5-72B-Instruct	0.9696	0.5699	0.5811	0.7381	0.1706	0.8360	0.2269	0.2179	0.7899	0.6256	0.5726
Llama 3 70B Instruct	0.9419	0.6114	0.5506	0.9164	0.1912	0.7200	0.2708	0.2350	0.6789	0.6610	0.5777
Llama 3.1 70B Instruct	0.9482	0.6246	0.5781	0.9201	0.1772	0.7440	0.2805	0.2472	0.7323	0.6933	0.5945
Llama 3 Youko 70B Instruct	0.9526	0.6252	0.5853	0.9215	0.1983	0.7400	0.2633	0.2245	0.7170	0.6098	0.5838
Llama-3.1-70B-Japanese-Instruct-2407	0.9562	0.6466	0.6602	0.9187	0.1564	0.7480	0.2901	0.2410	0.7227	0.6274	0.5967
Llama 3 heron brain 70B v0.3	0.9660	0.6643	0.6817	0.9221	0.2611	0.7720	0.3093	0.2578	0.7077	0.6079	0.6150
Llama 3 Swallow 70B Instruct	0.9607	0.6188	0.6026	0.9236	0.1389	0.6560	0.2724	0.2532	0.6572	0.6000	0.5683
Llama 3.1 Swallow 70B Instruct	0.9598	0.6192	0.6605	0.9235	0.1938	0.7760	0.3123	0.2593	0.7117	0.4713	0.5887

English tasks

Model	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
Qwen2-72B-Instruct	0.4360	0.7588	0.6857	0.3913	0.9110	0.8391	0.8499	0.2436	0.6939	0.6455
Qwen2.5-72B-Instruct	0.4540	0.6764	0.7064	0.3550	0.8895	0.8478	0.9113	0.4027	0.6165	0.6511
Llama 3 70B Instruct	0.4400	0.7999	0.6552	0.4024	0.9127	0.7992	0.9052	0.8326	0.7555	0.7225
Llama 3.1 70B Instruct	0.4300	0.8212	0.6621	0.3921	0.9157	0.8213	0.8764	0.8390	0.7915	0.7277
Llama 3 Youko 70B Instruct	0.4500	0.7973	0.6863	0.3914	0.9153	0.8055	0.8923	0.7814	0.6598	0.7088
Llama-3.1-70B-Japanese-Instruct-2407	0.4220	0.8104	0.6481	0.3744	0.9170	0.8071	0.8893	0.8228	0.7463	0.7153
Llama 3 heron brain 70B v0.3	0.4460	0.8107	0.6682	0.4085	0.9174	0.7898	0.8772	0.7586	0.6713	0.7053
Llama 3 Swallow 70B Instruct	0.4520	0.8174	0.6758	0.4050	0.9230	0.7883	0.8688	0.8152	0.6890	0.7150
Llama 3.1 Swallow 70B Instruct	0.4520	0.8148	0.6834	0.4012	0.9157	0.7855	0.8886	0.8486	0.5823	0.7080

MT-Bench JA

Model	coding	extraction	humanities	math	reasoning	roleplay	stem	writing	JMTAvg
Qwen2-72B-Instruct	0.5699	0.7858	0.8222	0.5096	0.7032	0.7963	0.7728	0.8223	0.7228
Qwen2.5-72B-Instruct	0.7060	0.7866	0.8122	0.6968	0.6536	0.8301	0.8060	0.7841	0.7594
Llama 3 70B Instruct	0.5969	0.8410	0.7120	0.4481	0.4884	0.7117	0.6510	0.6900	0.6424
Llama 3.1 70B Instruct	0.5252	0.7846	0.7086	0.5063	0.6979	0.6888	0.6402	0.6653	0.6521
Llama 3 Youko 70B Instruct	0.6632	0.8387	0.8108	0.4655	0.7013	0.7778	0.7544	0.7662	0.7222
Llama-3.1-70B-Japanese-Instruct-2407	0.6267	0.7525	0.7938	0.5750	0.5590	0.7725	0.7240	0.7180	0.6902
Llama 3 heron brain 70B v0.3	0.3762	0.7892	0.7274	0.5589	0.5070	0.6662	0.6880	0.6996	0.6266
Llama 3 Swallow 70B Instruct	0.5269	0.7250	0.5690	0.4669	0.6121	0.6238	0.5533	0.5698	0.5809
Llama 3.1 Swallow 70B Instruct	0.5676	0.7859	0.7490	0.5437	0.6383	0.6870	0.6121	0.6540	0.6547
GPT-3.5 (gpt-3.5-turbo-0125)	0.6851	0.7641	0.7414	0.5522	0.5128	0.7104	0.6266	0.7361	0.6661
GPT-4o (gpt-4o-2024-05-13)	0.7296	0.8540	0.8646	0.6641	0.6661	0.8274	0.8184	0.8085	0.7791

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 capabilities of multi-turn dialogue with the following settings:

• Implementation: 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-Leaderboard 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-3.1-Swallow-70B-Instruct-v0.1"

tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(
    model=model_name,
    tensor_parallel_size=4,
)

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": "東京の紅葉した公園で、東京タワーと高層ビルを背景に、空を舞うツバメと草地に佇むラマが出会う温かな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.

• Japanese
  • lmsys-chat-1m-synth-ja-wo-pii-and-template-instructions
    • Single-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 Llama-3.1-405B-Instruct. Llama-3.1-70B-Instruct served as a judge for rejection sampling (n=6). Conversations containing personally identifiable information (PII) and template-based user instructions were removed. Duplicate instructions were removed.
    • The dataset is available at tokyotech-llm/lmsys-chat-1m-synth.
  • filtered-magpie-ultra-ja
    • A Japanese variant of the filtered-magpie-ultra-en dataset, translated into Japanese by gemma-2-27b-it.
  • gemma-magpie
    • A Japanese synthetic Q&A 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 then heuristically filtered for quality and length.
• English
  • lmsys-chat-1m-synth-en-wo-pii-and-template-instructions
    • The creation process is similar to lmsys-chat-1m-synth-ja-wo-pii-and-template-instructions, but this version uses the original English user instructions. The assistant responses were generated in English as well. Rejection sampling was not applied for this version.
    • The dataset is available at tokyotech-llm/lmsys-chat-1m-synth.
  • filtered-magpie-ultra-en
    • A subset of the magpie-ultra dataset, developed following the MAGPIE recipe [Xu+, arXiv24] using Llama-3.1-405B-Instruct. This subset includes only samples rated as 'average,' 'good,' or 'excellent.'

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 3.1 under a generous open license.

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

License

META LLAMA 3.1 COMMUNITY LICENSE and Gemma Terms of Use

Authors

Here are the team members:

• From Tokyo Institute of Technology Okazaki Laboratory, the following members:
  • Naoaki Okazaki
  • Sakae Mizuki
  • Youmi Ma
  • Koki Maeda
  • Kakeru Hattori
  • Masanari Ohi
  • Taihei Shiotani
  • Koshiro Saito
• From Tokyo Institute of Technology YOKOTA Laboratory, the following members:
  • Rio Yokota
  • Kazuki Fujii
  • Taishi Nakamura
  • Takumi Okamoto
  • Ishida Shigeki
• From Artificial Intelligence Research Center, AIST, Japan, the following members:
  • Hiroya Takamura

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},
}

References

@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}, 
}