Llama 3.3 Swallow - Built with Llama

Llama 3.3 Swallow is a large language model (70B) that was built by continual pre-training on the Meta Llama 3.3 model. Llama 3.3 Swallow enhanced the Japanese language capabilities of the original Llama 3.3 while retaining the English language capabilities. We use approximately 315 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

March 10, 2025: Released Llama-3.3-Swallow-70B-Instruct-v0.4 and Llama-3.3-Swallow-70B-v0.4.
December 30, 2024: Released Llama-3.1-Swallow-70B-Instruct-v0.3.
December 23, 2024: Released Llama-3.1-Swallow-8B-Instruct-v0.3.
November 11, 2024: Released Llama-3.1-Swallow-8B-v0.2 and Llama-3.1-Swallow-8B-Instruct-v0.2.
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 v0.1	Llama-3.1-Swallow-Instruct v0.1	Llama-3.1-Swallow v0.2	Llama-3.1-Swallow-Instruct v0.2	Llama-3.1-Swallow-Instruct v0.3	Llama-3.3-Swallow v0.4	Llama-3.3-Swallow-Instruct v0.4
8B	🤗 HuggingFace	🤗 HuggingFace	🤗 HuggingFace	🤗 HuggingFace	🤗 HuggingFace
70B	🤗 HuggingFace	🤗 HuggingFace			🤗 HuggingFace	🤗 HuggingFace	🤗 HuggingFace

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

MT-Bench JA

Model	coding	extraction	humanities	math	reasoning	roleplay	stem	writing	JMT Avg
Llama 3 70B Instruct	0.588	0.884	0.715	0.637	0.487	0.594	0.598	0.619	0.640
Llama 3.1 70B Instruct	0.691	0.848	0.730	0.669	0.618	0.699	0.699	0.694	0.706
Llama 3.3 70B Instruct	0.707	0.865	0.757	0.720	0.635	0.773	0.706	0.733	0.737
Llama 3 Youko 70B Instruct	0.607	0.894	0.834	0.609	0.673	0.790	0.764	0.829	0.750
Llama-3.1-70B-Japanese-Instruct-24070	0.683	0.827	0.824	0.749	0.643	0.818	0.715	0.751	0.751
Llama 3 heron brain 70B v0.3	0.510	0.870	0.776	0.680	0.513	0.727	0.692	0.693	0.683
Llama 3 Swallow 70B Instruct	0.633	0.823	0.601	0.521	0.482	0.622	0.635	0.630	0.618
Llama 3.1 Swallow 70B Instruct v0.1	0.654	0.792	0.768	0.704	0.573	0.682	0.653	0.704	0.691
Llama 3.1 Swallow 70B Instruct v0.3	0.678	0.820	0.867	0.776	0.570	0.816	0.769	0.852	0.769
Llama 3.3 Swallow 70B Instruct v0.4	0.705	0.820	0.870	0.730	0.623	0.811	0.781	0.832	0.772
Qwen2-72B-Instruct	0.632	0.800	0.842	0.688	0.616	0.824	0.797	0.846	0.756
Qwen2.5-72B-Instruct	0.795	0.860	0.865	0.857	0.784	0.863	0.804	0.854	0.835
GPT-3.5 (gpt-3.5-turbo-0125)	0.693	0.789	0.773	0.665	0.462	0.728	0.644	0.775	0.691
GPT-4o (gpt-4o-2024-08-06)	0.855	0.926	0.880	0.872	0.706	0.862	0.838	0.849	0.848
GPT-4o-mini (gpt-4o-mini-2024-07-18)	0.825	0.865	0.857	0.843	0.665	0.846	0.855	0.840	0.824

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
Llama 3 70B Instruct	0.940	0.615	0.557	0.913	0.191	0.716	0.269	0.234	0.680	0.662	0.578
Llama 3.1 70B Instruct	0.950	0.635	0.579	0.921	0.178	0.732	0.279	0.247	0.733	0.696	0.595
Llama 3.3 70B Instruct	0.941	0.640	0.570	0.893	0.179	0.784	0.278	0.243	0.735	0.744	0.601
Llama 3 Youko 70B Instruct	0.952	0.625	0.584	0.921	0.198	0.720	0.263	0.226	0.718	0.610	0.582
Llama-3.1-70B-Japanese-Instruct-24070	0.956	0.647	0.660	0.919	0.156	0.748	0.290	0.241	0.723	0.627	0.597
Llama 3 heron brain 70B v0.3	0.965	0.652	0.679	0.922	0.261	0.772	0.309	0.258	0.707	0.623	0.615
Llama 3 Swallow 70B Instruct	0.963	0.627	0.598	0.921	0.139	0.672	0.272	0.255	0.657	0.608	0.571
Llama 3.1 Swallow 70B Instruct v0.1	0.962	0.621	0.660	0.924	0.192	0.776	0.312	0.259	0.711	0.468	0.588
Llama 3.1 Swallow 70B Instruct v0.3	0.964	0.632	0.654	0.911	0.196	0.772	0.305	0.257	0.690	0.596	0.598
Llama 3.3 Swallow 70B Instruct v0.4	0.981	0.618	0.662	0.907	0.162	0.812	0.319	0.261	0.707	0.700	0.613
Qwen2-72B-Instruct	0.963	0.628	0.557	0.920	0.166	0.780	0.260	0.232	0.771	0.701	0.598
Qwen2.5-72B-Instruct	0.970	0.569	0.582	0.738	0.170	0.840	0.227	0.218	0.789	0.634	0.574
GPT-3.5 (gpt-3.5-turbo-0125)	0.922	0.456	0.447	0.893	0.215	0.572	0.287	0.243	0.499	0.616	0.515
GPT-4o (gpt-4o-2024-08-06)	0.982	0.731	0.709	0.889	0.170	0.864	0.314	0.254	0.797	0.752	0.646
GPT-4o-mini (gpt-4o-mini-2024-07-18)	0.961	0.464	0.591	0.902	0.160	0.832	0.299	0.241	0.679	0.675	0.580

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
Llama 3 70B Instruct	0.438	0.800	0.655	0.696	0.914	0.800	0.909	0.474	0.833	0.774	0.729
Llama 3.1 70B Instruct	0.426	0.821	0.662	0.660	0.917	0.822	0.876	0.560	0.842	0.794	0.738
Llama 3.3 70B Instruct	0.426	0.817	0.667	0.684	0.917	0.824	0.890	0.706	0.853	0.834	0.762
Llama 3 Youko 70B Instruct	0.454	0.797	0.686	0.659	0.915	0.805	0.892	0.434	0.780	0.662	0.708
Llama-3.1-70B-Japanese-Instruct-24070	0.422	0.810	0.647	0.663	0.917	0.807	0.889	0.528	0.823	0.746	0.725
Llama 3 heron brain 70B v0.3	0.446	0.811	0.668	0.706	0.919	0.790	0.877	0.508	0.759	0.668	0.715
Llama 3 Swallow 70B Instruct	0.446	0.818	0.676	0.681	0.923	0.789	0.868	0.460	0.816	0.680	0.716
Llama 3.1 Swallow 70B Instruct v0.1	0.446	0.815	0.683	0.681	0.917	0.787	0.884	0.474	0.848	0.568	0.710
Llama 3.1 Swallow 70B Instruct v0.3	0.454	0.825	0.692	0.647	0.919	0.777	0.872	0.458	0.816	0.643	0.710
Llama 3.3 Swallow 70B Instruct v0.4	0.448	0.817	0.686	0.654	0.912	0.803	0.908	0.566	0.812	0.750	0.736
Qwen2-72B-Instruct	0.444	0.759	0.685	0.685	0.911	0.839	0.848	0.634	0.193	0.688	0.669
Qwen2.5-72B-Instruct	0.454	0.676	0.706	0.677	0.889	0.848	0.904	0.770	0.375	0.614	0.691

Evaluation Benchmarks

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_v4
Reference Answer: A revised version of Nejumi LLM-Leaderboard NEO, mtbench_ja_referenceanswer_v2, in which we verified and corrected incorrect answers. This revised version will be released alongside swallow-evaluation Ver. 202411.
Prompt for Judge: Nejumi LLM-Leaderboard NEO, mtbench_ja_prompt_v1
Judge: gpt-4o-2024-08-06
Scoring: Absolute scale normalized to a 0-1 range, averaged over five runs.

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])

Usage

pip install vllm

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

model_name = "tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4"

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": "日本の冬から春の移り変わりについて教えてください",
    },
]
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.

Gemma-2-LMSYS-Chat-1M-Synth
- Multi-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 gemma-2-27b-it. The same model, i.e., gemma-2-27b-it served as a judge for rejection sampling (n=6).
- Second-turn user instructions and responses were synthesized using gemma-2-27b-it. The same model scores the quality of the second-turn response with a range of 1-10. Second-turn responses with scores lower than 9 were rejected, along with their corresponding instructions.
  Conversations containing personally identifiable information (PII) and template-based user instructions were removed. Duplicate instructions were removed.
Swallow-Magpie-Ultra-v0.1
- A Japanese variant of the filtered-magpie-ultra-en dataset, translated into Japanese by gemma-2-27b-it.
Swallow-Gemma-Magpie-v0.1
- A Japanese synthetic instruction tuning 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 heuristically filtered for quality and length. Then, gemma-2-27b-it was applied to score the quality of each of the conversation with a range of 1-10. Conversations with scores <= 7 were rejected.
Swallow-Code-v0.3-Instruct-style
- A synthetic instruction dataset for code generation in English, restructured into an instruction-following format from Swallow Code v0.3 using Llama-3.3-70B-Instruct.

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, which enabled the training of the Llama 3.3 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

License

META LLAMA 3.3 COMMUNITY LICENSE and Gemma Terms of Use

Authors

Here are the team members:

From Institute of Science Tokyo Okazaki Laboratory, the following members:
From Institute of Science Tokyo YOKOTA Laboratory, the following members:
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}, 
}

tokyotech-llm
/

Llama-3.3-Swallow-70B-Instruct-v0.4

Llama 3.3 Swallow - Built with Llama

Release History

Swallow Model Index

Model Details

Model Performance

MT-Bench JA

Japanese tasks

English tasks

Evaluation Benchmarks

MT-Bench JA

Japanese evaluation benchmarks

English evaluation benchmarks

Usage

Training Datasets

Instruction Tuning

Risks and Limitations

Acknowledgements

License

Authors

How to cite

References

Model tree for tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4

Datasets used to train tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4

Collection including tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4

Llama-3.3-Swallow