`Llama 3 Youko 70B (rinna/llama-3-youko-70b)`

Overview

We conduct continual pre-training of meta-llama/Meta-Llama-3-70B on 5B 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 80-layer, 8192-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-3-70B model and continually trained on around 5B tokens from a mixture of the following corpora
- Japanese CC-100
- Japanese C4
- Japanese OSCAR
- The Pile
- Wikipedia
- rinna curated Japanese dataset
Contributors
Release date

July 25, 2024

Benchmarking

Please refer to rinna's LM benchmark page (Sheet 20240725).

How to use the model

import transformers
import torch

model_id = "rinna/llama-3-youko-70b"
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"])

Tokenization

The model uses the original meta-llama/Meta-Llama-3-70B tokenizer.

How to cite

@misc{rinna-llama-3-youko-70b,
 title = {rinna/llama-3-youko-70b},
 author = {Mitsuda, Koh and Chen, Xinqi and Wakatsuki, Toshiaki and Sawada, Kei},
 url = {https://huggingface.co/rinna/llama-3-youko-70b}
}

@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}}
}

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}
}