japanese-gpt-neox-3.6b-instruction-sft

Overview

This repository provides a Japanese GPT-NeoX model of 3.6 billion parameters. The model is based on rinna/japanese-gpt-neox-3.6b and has been finetuned to serve as an instruction-following conversational agent.

• Model architecture

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

• Finetuning

  The finetuning data is the subset of the following datasets and has been translated into Japanese.

  • Anthropic HH RLHF data
  • FLAN Instruction Tuning data
  • Stanford Human Preferences Dataset

  The data will not be released.

• Model Series

  Variant	Link
  3.6B PPO	https://huggingface.co./rinna/japanese-gpt-neox-3.6b-instruction-ppo
  3.6B SFT-v2	https://huggingface.co./rinna/japanese-gpt-neox-3.6b-instruction-sft-v2
  3.6B SFT	https://huggingface.co./rinna/japanese-gpt-neox-3.6b-instruction-sft
  3.6B pretrained	https://huggingface.co./rinna/japanese-gpt-neox-3.6b

• Contributors

  Tianyu Zhao and Kei Sawada

I/O Format

A special format has been adopted to construct inputs.

• An input prompt is formatted as a conversation between ユーザー and システム.
• Each input utterance consists of (1) its speaker ("ユーザー" or "システム"), (2) a colon (":"), (3) a whitespace (" "), and (4) utterance text (e.g. "世界で一番高い山は？").
• The input prompt should be ended with "システム: " to acknowledge the model to generate a response.
• Since the model's tokenizer does not recognize "\n", a special newline symbol "<NL>" is used instead.
• All the newlines in input and output utterances should be replaced with "<NL>".
• All the utterances in the input prompt should be separated by "<NL>".

Following is an example to construct an input from a conversation.

prompt = [
    {
        "speaker": "ユーザー",
        "text": "日本のおすすめの観光地を教えてください。"
    },
    {
        "speaker": "システム",
        "text": "どの地域の観光地が知りたいですか？"
    },
    {
        "speaker": "ユーザー",
        "text": "渋谷の観光地を教えてください。"
    }
]
prompt = [
    f"{uttr['speaker']}: {uttr['text']}"
    for uttr in prompt
]
prompt = "<NL>".join(prompt)
prompt = (
    prompt
    + "<NL>"
    + "システム: "
)
print(prompt)
# "ユーザー: 日本のおすすめの観光地を教えてください。<NL>システム: どの地域の観光地が知りたいですか？<NL>ユーザー: 渋谷の観光地を教えてください。<NL>システム: "

How to use the model

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

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

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

token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt")

with torch.no_grad():
    output_ids = model.generate(
        token_ids.to(model.device),
        do_sample=True,
        max_new_tokens=128,
        temperature=0.7,
        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][token_ids.size(1):])
output = output.replace("<NL>", "\n")
print(output)
"""分かりました。いくつかのおすすめを紹介します。
1. ハチ公像です。ハチ公像は、日本の観光スポットの1つとして人気があります。
2. スクランブル交差点です。多くの人々が行き交う大きな交差点で、観光客に人気のスポットです。
3. 109です。109は、ショッピングやエンターテイメント施設です。
4. 道玄坂です。道玄坂は、日本の商業地区である坂道です。</s>"""

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>'
  

How to cite

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

@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