---
language:
  - en
  - ja
library_name: transformers
pipeline_tag: text-generation
model_type: mistral
license: apache-2.0
---

# Swallow-MS-7b-v0.1

Our Swallow-MS-7b-v0.1 model has undergone continuous 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](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-instruct-v0.1)
- **March 11, 2024**: Released the [Swallow-MS-7b-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-v0.1)
![logo](./logo.png)

This repository provides large language models developed by [TokyoTech-LLM](https://tokyotech-llm.github.io/).

## 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 

## Instruct Model Performance

### MT-Bench JA

TODO

## Base Model Performance


## 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](./requirements.txt):

```sh
pip install -r requirements.txt
```

### Instruction format Ver1.0
This format must be adhered to strictly, as deviations may result in less optimal outputs from the model.

The template used to construct a prompt for the Instruct model is specified as follows:

```
<s>[INST] <<SYS>>\n{Instruction}\n<</SYS>>\n\n{USER_MESSAGE_1} [INST] {BOT_MESSAGE_1} </s>[INST] {USER_MESSAGE_2}[/INST] 
```

Please be aware that  ``<s> `` and  ``</s> `` are special tokens used for the beginning of string (BOS) and end of string (EOS), respectively, while [INST] and [/INST] are considered regular strings.

### Use the instruct model Ver1.0

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "tokyotech-llm/Swallow-MS-7b-instruct-v1.0"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

device = "cuda"

messages = [
    {"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": "東京工業大学の主なキャンパスについて教えてください"}
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=128, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```


### Use the base model

```python
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](https://huggingface.co/datasets/EleutherAI/proof-pile-2)
- [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch)
- [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
- [Swallow Corpus](https://chokkan.org/temp/tokyotech-llm/swallow-corpus)
- [The Pile](https://huggingface.co/datasets/EleutherAI/pile)

### Instruction Tuning

#### Ver1.0

The following datasets were used for the instruction tuning. 

- [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/datasets/llm-jp/oasst1-21k-jahttps://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) model.
- [OpenAssistant Conversations Dataset 21k Ja](https://huggingface.co/datasets/llm-jp/oasst1-21k-ja)
- [OpenAssistant Conversations Dataset 21k En](https://huggingface.co/datasets/llm-jp/oasst1-21k-en)
- [Databricks Dolly 15k Ja](https://huggingface.co/datasets/llm-jp/databricks-dolly-15k-ja)
- [Databricks Dolly 15k En](https://huggingface.co/datasets/databricks/databricks-dolly-15k)

Please note that some of the data had issues with quality or format, so not all of it was used.

## 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](https://abci.ai/en/link/llm_support_program.html) of the National Institute of Advanced Industrial Science and Technology. 

## License

apache-2.0

## Authors

Here are the team members:
- From [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/)
  - [Hiroki Iida](https://meshidenn.github.io/)
  - [Mengsay Loem](https://loem-ms.github.io/)
  - [Shota Hirai](https://huggingface.co/Kotemo428)
  - [Kakeru Hattori](https://aya-se.vercel.app/)
  - [Masanari Ohi](https://twitter.com/stjohn2007)
- From [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)