Japanese Gpt Neox 3.6b

🚀 Japanese GPT-NeoX-3.6B

This repository offers a Japanese GPT-NeoX model with 3.6 billion parameters, trained on extensive datasets to optimize language modeling.

🚀 Quick Start

To start using the japanese-gpt-neox-3.6b model, you can follow the code example below:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt-neox-3.6b", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt-neox-3.6b")

if torch.cuda.is_available():
    model = model.to("cuda")

text = "西田幾多郎は、"
token_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt")

with torch.no_grad():
    output_ids = model.generate(
        token_ids.to(model.device),
        max_new_tokens=100,
        min_new_tokens=100,
        do_sample=True,
        temperature=0.8,
        pad_token_id=tokenizer.pad_token_id,
        bos_token_id=tokenizer.bos_token_id,
        eos_token_id=tokenizer.eos_token_id
    )

output = tokenizer.decode(output_ids.tolist()[0])
print(output)
"""西田幾多郎は、この「絶対矛盾的自己同一」を「世界の自己同一」と置きかえ、さらに西田哲学を出発点として「絶対無」を「世界の成立」に変え、世界と自己を一つの統一物とみなす哲学として展開する。この世界と自己は絶対矛盾的自己同一として同一の性質を有し、同じ働きをする。西田哲学においては、この世界と自己は矛盾しあうのではなく、同一の性質をもっている。この世界と自己は同一である。絶対"""

✨ Features

Library

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

Model architecture

A 36 - layer, 2816 - hidden - size transformer - based language model.

Pre - training

The model was trained on around 312.5B tokens from Japanese CC - 100, Japanese C4, and Japanese Wikipedia to optimize a traditional language modelling objective. A final validation perplexity of 8.68 has been reached.

Model Series

Contributors

Tianyu Zhao and Kei Sawada

Release date

March 17, 2023

📚 Documentation

Tokenization

The model uses a sentencepiece - based tokenizer.

• The tokenizer has a vocabulary size of 32,000.
• It uses sentencepiece's byte fallback feature to decompose unknown text pieces into UTF - 8 byte pieces and to avoid producing <UNK> tokens.
• sentencepiece's --add_dummy_prefix option was turned off so that a leading whitespace will not be prepended automatically.

  print(tokenizer.tokenize("吾輩は猫である"))
  # ['吾', '輩', 'は', '猫', 'である']
  # instead of ['▁', '吾', '輩', 'は', '猫', 'である'] as in rinna/japanese-gpt-1b
  

• sentencepiece's --remove_extra_whitespaces option was turned off so that leading, trailing, and duplicate whitespaces are reserved.

  print(tokenizer.tokenize("  吾輩は  猫である   "))
  # ['▁', '▁', '吾', '輩', 'は', '▁', '▁', '猫', 'である', '▁', '▁', '▁']
  # instead of ['▁', '吾', '輩', 'は', '▁猫', 'である'] as in rinna/japanese-gpt-1b
  

• Don't forget to set use_fast=False to make the above features function correctly.

  good_tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt-neox-3.6b", use_fast=False)
  bad_tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt-neox-3.6b")
  
  print(good_tokenizer.decode(good_tokenizer.encode("გამარჯობა  吾輩は  猫である   ")))
  # 'გამარჯობა  吾輩は  猫である   </s>'
  print(bad_tokenizer.decode(bad_tokenizer.encode("გამარჯობა  吾輩は  猫である   ")))
  # 'გამარ[UNK]ობა 吾輩は 猫である </s>'
  

📄 License

The MIT license

How to cite

@misc{rinna-japanese-gpt-neox-3.6b,
    title = {rinna/japanese-gpt-neox-3.6b},
    author = {Zhao, Tianyu and Sawada, Kei},
    url = {https://huggingface.co/rinna/japanese-gpt-neox-3.6b}
}

@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}}
}