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Swallow-MX-8x7b-NVE-v0.1

Text Generation
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metadata
language:
  - en
  - ja
library_name: transformers
pipeline_tag: text-generation
tag: moe
license: apache-2.0

Swallow-MX-8x7b-NVE-v0.1

Our Swallow-MX-8x7b-NVE-v0.1 model has undergone continuous pre-training from the Mixtral-8x7B-Instruct-v0.1, primarily with the addition of Japanese language data.

logo

Model Details

  • Model type: Please refer to Mixtral technical report for details on the model architecture.
  • Language(s): Japanese English
  • Tokenizer: This model utilizes the same tokenizer as employed by Mixtral-8x7B-Instruct-v0.1.
  • 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
4-shot 4-shot 4-shot 4-shot 1-shot 4-shot 4-shot 4-shot
Llama 2 7B 0.3852 0.4240 0.3410 0.7917 0.1905 0.0760 0.1783 0.1738
Swallow 7B 0.4808 0.5078 0.5968 0.8573 0.1830 0.1240 0.2510 0.1511
Swallow-Plus 7B 0.5478 0.5493 0.6030 0.8544 0.1806 0.1360 0.2568 0.1441
Swallow-NVE 7B 0.5433 0.5425 0.5729 0.8684 0.2117 0.1200 0.2405 0.1512
Mistral-7B-v0.1 7B 0.7301 0.4245 0.2722 0.8563 0.2006 0.1760 0.1405 0.1733
Swallow-MS-7b-v0.1 7B 0.8570 0.4915 0.5519 0.8802 0.1988 0.2240 0.2494 0.1667
Llama 2 13B 0.6997 0.4415 0.4170 0.8533 0.2139 0.1320 0.2146 0.1982
Swallow 13B 0.7837 0.5063 0.6398 0.9005 0.2168 0.2040 0.2720 0.1771
Swallow-NVE 13B 0.7712 0.5438 0.6351 0.9030 0.2294 0.2120 0.2735 0.1817
Llama 2 70B 0.8686 0.4656 0.5256 0.9080 0.2361 0.3560 0.2643 0.2398
Swallow 70B 0.9348 0.6290 0.6960 0.9176 0.2266 0.4840 0.3043 0.2298
Swallow-NVE 70B 0.9410 0.5759 0.7024 0.9254 0.2758 0.4720 0.3042 0.2322
Mixtral-8x7B-v0.1 8x7B 0.8347 0.5335 0.3549 0.8847 0.2192 0.3120 0.1970 0.1987
Swallow-MX-8x7b-NVE-v0.1 8x7B 0.9258 0.5843 0.5687 0.9148 0.2589 0.4360 0.2705 0.2074

English version

Model Size OpenBookQA TriviaQA HellaSwag SQuAD2.0 XWINO GSM8K
8-shot 8-shot 8-shot 8-shot 8-shot 8-shot
Llama 2 7B 0.3580 0.6265 0.5860 0.3207 0.9049 0.1410
Swallow 7B 0.3180 0.4836 0.5308 0.3125 0.8817 0.1130
Swallow-Plus 7B 0.3280 0.4558 0.5259 0.3134 0.8929 0.1061
Swallow-NVE 7B 0.3180 0.5079 0.5329 0.2919 0.8817 0.0986
Mistral-7B-v0.1 7B 0.3660 0.7050 0.6264 0.3799 0.9157 0.3533
Swallow-MS-7b-v0.1 7B 0.3440 0.5976 0.5810 0.3364 0.9037 0.2623
Llama 2 13B 0.3760 0.7255 0.6148 0.3681 0.9140 0.2403
Swallow 13B 0.3500 0.5852 0.5660 0.3406 0.9075 0.2039
Swallow-NVE 13B 0.3460 0.6025 0.5700 0.3478 0.9006 0.1751
Llama 2 70B 0.4280 0.8239 0.6742 0.3770 0.9290 0.5284
Swallow 70B 0.4220 0.7756 0.6458 0.3745 0.9204 0.4867
Swallow-NVE 70B 0.4240 0.7817 0.6439 0.3451 0.9256 0.4943
Mixtral-8x7B-v0.1 8x7B 0.3960 0.7989 0.6678 0.3842 0.9204 0.5747
Swallow-MX-8x7b-NVE-v0.1 8x7B 0.3740 0.7847 0.6520 0.3801 0.9170 0.5694

Please note that Swallow-MX-8x7b-NVE-v0.1 is not derived from Mixtral-8x7B-v0.1, but rather underwent continued pre-training from Mixtral-8x7B-Instruct-v0.1.

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-MX-8x7b-NVE-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.

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 Mixtral-8x7B-Instruct-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: