rinna_-_japanese-gpt2-xsmall-4bits

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japanese-gpt2-xsmall - bnb 4bits

• Model creator: https://huggingface.co/rinna/
• Original model: https://huggingface.co/rinna/japanese-gpt2-xsmall/

Original model description:

language: ja thumbnail: https://github.com/rinnakk/japanese-gpt2/blob/master/rinna.png tags:

• gpt2
• text-generation
• lm
• nlp license: mit datasets:
• cc100
• wikipedia widget:
• text: "生命、宇宙、そして万物についての究極の疑問の答えは"

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japanese-gpt2-xsmall

This repository provides an extra-small-sized Japanese GPT-2 model. The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd.

How to use the model

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt2-xsmall", use_fast=False)
tokenizer.do_lower_case = True  # due to some bug of tokenizer config loading

model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt2-xsmall")

Model architecture

A 6-layer, 512-hidden-size transformer-based language model.

Training

The model was trained on Japanese CC-100 and Japanese Wikipedia to optimize a traditional language modelling objective on 8\*V100 GPUs for around 4 days. It reaches around 28 perplexity on a chosen validation set from CC-100.

Tokenization

The model uses a sentencepiece-based tokenizer, the vocabulary was trained on the Japanese Wikipedia using the official sentencepiece training script.

How to cite

@misc{rinna-japanese-gpt2-xsmall,
    title = {rinna/japanese-gpt2-xsmall},
    author = {Zhao, Tianyu and Sawada, Kei},
    url = {https://huggingface.co/rinna/japanese-gpt2-xsmall}
}

@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}}
}

Licenese

The MIT license