Llama 3 Youko 8B (rinna/llama-3-youko-8b)

Overview

We conduct continual pre-training of meta-llama/Meta-Llama-3-8B on 22B tokens from a mixture of Japanese and English datasets. The continual pre-training significantly improves the model's performance on Japanese tasks.

The name youko comes from the Japanese word 妖狐/ようこ/Youko, which is a kind of Japanese mythical creature (妖怪/ようかい/Youkai).

Size	Continual Pre-Training	Instruction-Tuning
8B	Llama 3 Youko 8B [HF] [GPTQ]	Llama 3 Youko 8B Instruct [HF] [GPTQ]
70B	Llama 3 Youko 70B [HF] [GPTQ]	Llama 3 Youko 70B Instruct [HF] [GPTQ]

• Library

  The model was trained using code based on EleutherAI/gpt-neox.

• Model architecture

  A 32-layer, 4096-hidden-size transformer-based language model. Refer to the Llama 3 Model Card for architecture details.

• Training: Built with Meta Llama 3

  The model was initialized with the meta-llama/Meta-Llama-3-8B model and continually trained on around 22B tokens from a mixture of the following corpora

  • Japanese CC-100
  • Japanese C4
  • Japanese OSCAR
  • The Pile
  • Wikipedia
  • rinna curated Japanese dataset

• Contributors

  • Koh Mitsuda
  • Xinqi Chen
  • Toshiaki Wakatsuki
  • Kei Sawada

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Benchmarking

Please refer to rinna's LM benchmark page.

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How to use the model

import transformers
import torch

model_id = "rinna/llama-3-youko-8b"
pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto"
)
output = pipeline(
    "西田幾多郎は、",
    max_new_tokens=256,
    do_sample=True
)
print(output[0]["generated_text"])

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Tokenization

The model uses the original meta-llama/Meta-Llama-3-8B tokenizer.

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How to cite

@misc{rinna-llama-3-youko-8b,
    title = {rinna/llama-3-youko-8b},
    author = {Mitsuda, Koh and Chen, Xinqi and Wakatsuki, Toshiaki and Sawada, Kei},
    url = {https://huggingface.co./rinna/llama-3-youko-8b}
}

@inproceedings{sawada2024release,
    title = {Release of Pre-Trained Models for the {J}apanese Language},
    author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
    booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
    month = {5},
    year = {2024},
    pages = {13898--13905},
    url = {https://aclanthology.org/2024.lrec-main.1213},
    note = {\url{https://arxiv.org/abs/2404.01657}}
}

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References

@article{llama3modelcard,
    title = {Llama 3 Model Card},
    author = {AI@Meta},
    year = {2024},
    url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}

@software{gpt-neox-library,
    title = {{GPT}-{N}eo{X}: Large Scale Autoregressive Language Modeling in {P}y{T}orch},
    author = {Andonian, Alex and Anthony, Quentin and Biderman, Stella and Black, Sid and Gali, Preetham and Gao, Leo and Hallahan, Eric and Levy-Kramer, Josh and Leahy, Connor and Nestler, Lucas and Parker, Kip and Pieler, Michael and Purohit, Shivanshu and Songz, Tri and Phil, Wang and Weinbach, Samuel},
    doi = {10.5281/zenodo.5879544},
    month = {8},
    year = {2021},
    version = {0.0.1},
    url = {https://www.github.com/eleutherai/gpt-neox}
}

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License

Meta Llama 3 Community License