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Swallow-MS-7b-v0.1

Text Generation
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mistral
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metadata
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. The instruction tuning version will be released soon.

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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 version

Model Size JCommonsenseQA JEMHopQA NIILC JSQuAD XL-Sum MGSM WMT20-en-ja WMT20-ja-en Average
4-shot 4-shot 4-shot 4-shot 1-shot 4-shot 4-shot 4-shot
CyberAgentLM2-7B 7B 0.2198 0.5047 0.5066 0.7799 0.0233 0.0600 0.2345 0.1499 0.3098
Llama 2 7B 0.3852 0.4240 0.3410 0.7917 0.1905 0.0760 0.1783 0.1738 0.3201
japanese-stablelm-base-beta-7b 7B 0.3610 0.4478 0.4432 0.8318 0.2195 0.0720 0.1946 0.1226 0.3366
japanese-stablelm-base-ja_vocab-beta-7b 7B 0.2172 0.4482 0.4309 0.8202 0.0757 0.0520 0.1601 0.1453 0.2937
ELYZA-japanese-Llama-2-7b 7B 0.5791 0.4703 0.4019 0.8226 0.1312 0.0600 0.1795 0.1289 0.3467
ELYZA-japanese-Llama-2-7b-fast 7B 0.5308 0.4330 0.3898 0.8131 0.1289 0.0720 0.1678 0.1143 0.3312
youri-7b (base) 7B 0.4620 0.4776 0.4999 0.8506 0.1957 0.0640 0.2671 0.1971 0.3768
Swallow-7b 7B 0.4808 0.5078 0.5968 0.8573 0.1830 0.1240 0.2510 0.1511 0.3940
Swallow-7b-plus 7B 0.5478 0.5493 0.6030 0.8544 0.1806 0.1360 0.2568 0.1441 0.4090
Qwen-7B 7B 0.7712 0.4234 0.2376 0.8594 0.1371 0.2160 0.1689 0.1801 0.3742
nekomata-7b 7B 0.7417 0.4928 0.5022 0.8707 0.1676 0.1240 0.2673 0.1815 0.4185
Mistral-7B-v0.1 7B 0.7301 0.4245 0.2722 0.8563 0.2006 0.1760 0.1405 0.1733 0.3717
japanese-stablelm-base-gamma-7b 7B 0.7364 0.4643 0.5568 0.8910 0.2293 0.1680 0.2390 0.1561 0.4301
Swallow-MS-7b-v0.1 7B 0.8570 0.4915 0.5519 0.8802 0.1988 0.2240 0.2494 0.1667 0.4524

English version

Model Size OpenBookQA TriviaQA HellaSwag SQuAD2.0 XWINO GSM8K Average
8-shot 8-shot 8-shot 8-shot 8-shot 8-shot
CyberAgentLM2-7B 7B 0.2860 0.3496 0.5003 0.3510 0.8581 0.0705 0.4026
Llama 2 7B 0.3580 0.6265 0.5860 0.3207 0.9049 0.1410 0.4895
japanese-stablelm-base-beta-7b 7B 0.3620 0.5903 0.5707 0.2992 0.8994 0.1198 0.4736
japanese-stablelm-base-ja_vocab-beta-7b 7B 0.3520 0.5549 0.5644 0.3079 0.8942 0.0538 0.4545
ELYZA-japanese-Llama-2-7b 7B 0.3400 0.5875 0.5595 0.2721 0.8989 0.1638 0.4703
ELYZA-japanese-Llama-2-7b-fast 7B 0.3280 0.5817 0.5530 0.2605 0.8989 0.1425 0.4608
youri-7b (base) 7B 0.3400 0.5257 0.5540 0.3297 0.8938 0.0963 0.4566
Swallow-7b 7B 0.3180 0.4836 0.5308 0.3125 0.8817 0.1130 0.4399
Swallow-7b-plus 7B 0.3280 0.4558 0.5259 0.3134 0.8929 0.1061 0.4370
Qwen-7B 7B 0.3640 0.5695 0.5787 0.3799 0.8933 0.4617 0.5412
nekomata-7b 7B 0.3340 0.4371 0.5340 0.2933 0.8766 0.1531 0.4380
Mistral-7B-v0.1 7B 0.3660 0.7050 0.6264 0.3799 0.9157 0.3533 0.5577
japanese-stablelm-base-gamma-7b 7B 0.3240 0.5745 0.5739 0.3546 0.8976 0.1911 0.4860
Swallow-MS-7b-v0.1 7B 0.3440 0.5976 0.5810 0.3364 0.9037 0.2623 0.5042

Code version

Model Size JHumanEval HumanEval
pass@1 pass@1
CyberAgentLM2-7B 7B
Llama 2 7B
japanese-stablelm-base-beta-7b 7B
japanese-stablelm-base-ja_vocab-beta-7b 7B
ELYZA-japanese-Llama-2-7b 7B
ELYZA-japanese-Llama-2-7b-fast 7B
youri-7b (base) 7B
Swallow-7b 7B
Swallow-7b-plus 7B
Qwen-7B 7B
nekomata-7b 7B
Mistral-7B-v0.1 7B
japanese-stablelm-base-gamma-7b 7B
Swallow-MS-7b-v0.1 7B

Usage

First install additional dependencies in requirements.txt:

pip install -r requirements.txt

Use the base model 

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "tokyotech-llm/Swallow-MS-7b-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")

model = AutoModelForCausalLM.from_pretrained(model_name)
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.

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: