Swallow-MS-7b-v0.1

Our Swallow-MS-7b-v0.1 model has undergone continual pre-training from the Mistral-7B-v0.1, primarily with the addition of Japanese language data.

Model Release Updates

We are excited to share the release schedule for our latest models:

• April 26, 2024: Released the Swallow-MS-7b-instruct-v0.1
• March 11, 2024: Released the Swallow-MS-7b-v0.1

This repository provides large language models developed by TokyoTech-LLM.

Model Details

• Model type: Please refer to Mistral technical report for details on the model architecture.
• Language(s): Japanese English
• Tokenizer: This model employs a tokenizer that features a broadened vocabulary based on Japanese data. This allows for a more efficient representation of text using fewer tokens, leading to a notably faster inference process.
• Contact: swallow[at]nlp.c.titech.ac.jp

Base Model Performance

Japanese tasks

English tasks

Code generation tasks

Evaluation Benchmarks

Japanese evaluation benchmarks

We used llm-jp-eval(v1.0.0) and JP Language Model Evaluation Harness(commit #9b42d41). The details are as follows:

• Multiple-choice question answering (JCommonsenseQA [Kurihara+, 2022])
• Open-ended question answering (JEMHopQA [Ishii+, 2023])
• Open-ended question answering (NIILC [Sekine, 2003])
• Machine reading comprehension (JSQuAD [Kurihara+, 2022])
• Automatic summarization (XL-Sum [Hasan+, 2021])
• Machine translation (WMT2020 ja-en [Barrault+, 2020])
• Machine translation (WMT2020 en-ja [Barrault+, 2020])
• Mathematical reasoning (MGSM [Shi+, 2023])

English evaluation benchmarks

We used the Language Model Evaluation Harness(v.0.3.0). The details are as follows:

• Multiple-choice question answering (OpenBookQA [Mihaylov+, 2018])
• Open-ended question answering (TriviaQA [Joshi+, 2017])
• Machine reading comprehension (SQuAD 2.0 [Rajpurkar+, 2018])
• Commonsense reasoning (XWINO [Tikhonov & Ryabinin, 2021])
• Natural language inference (HellaSwag [Zellers+, 2019])
• Mathematical reasoning (GSM8k [Cobbe+, 2021])

Code evaluation benchmarks

We utilized the Code Generation LM Evaluation Harness [Allal+, 2022] (commit #0261c52). The details are as follows:

• Code generation (HumanEval [Chen+, 2021])
• Code generation in Japanese (JHumanEval [Satoh+, 2024])

Usage

First install additional dependencies in requirements.txt:

pip install -r requirements.txt

Use the base model

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "tokyotech-llm/Swallow-MS-7b-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
prompt = "東京工業大学の主なキャンパスは、"
input_ids = tokenizer.encode(
    prompt,
    add_special_tokens=False,
    return_tensors="pt"
)
tokens = model.generate(
    input_ids.to(device=model.device),
    max_new_tokens=128,
    temperature=0.99,
    top_p=0.95,
    do_sample=True,
)

out = tokenizer.decode(tokens[0], skip_special_tokens=True)
print(out)

Training Datasets

Continual Pre-Training

The following datasets were used for continual pre-training.

• Algebraic Stack
• Japanese Wikipedia
• RefinedWeb
• Swallow Corpus
• The Pile

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 Mistral AI for releasing Mistral 7B v0.1 under an open license for others to build on.

Our project is supported by the ABCI Large-scale Language Model Building Support Program of the National Institute of Advanced Industrial Science and Technology.

License

apache-2.0

Authors

Here are the team members:

• From Okazaki Laboratory, the following members:
  • Naoaki Okazaki
  • Sakae Mizuki
  • Hiroki Iida
  • Mengsay Loem
  • Shota Hirai
  • Kakeru Hattori
  • Masanari Ohi
• From YOKOTA Laboratory, the following members:
  • Rio Yokota
  • Kazuki Fujii
  • Taishi Nakamura

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},
}