bilingual-gpt-neox-4b-instruction-sft

Update

• 2023/08/02 We uploaded the newly trained rinna/bilingual-gpt-neox-4b-instruction-sft with the MIT license.
  • Please refrain from using the previous model released on 2023/07/31 for commercial purposes if you have already downloaded it.
  • The new model released on 2023/08/02 is built from datasets with less strict licenses and has better evaluation performance, so we suggest using the new model.
  • For reference, we provide the MD5 checksum values for the pytorch_model.bin files of the previous and current models.
    • 2023/07/31 model: edf190a323c0ae63f71476700fb0b462
    • 2023/08/02 model: de72aa5b66beee7b65783c96f687d186
• 2023/07/31 In the previously released rinna/bilingual-gpt-neox-4b-instruction-sft, we found that part of the training data (i.e. Openchat ShareGPT4 and WizardLM) have a non-commercial license, and thus it does not comply with the MIT license. We decided to remove the previous version and build a new SFT model from datasets with less strict licenses. The new model will be uploaded in a few days. We sincerely apologize for our careless mistake.

Overview

This repository provides an English-Japanese bilingual GPT-NeoX model of 3.8 billion parameters.

The model is based on rinna/bilingual-gpt-neox-4b and has been finetuned to serve as an instruction-following conversational agent.

• Model architecture

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

• Fine-tuning

  The fine-tuning data is the subset of the following datasets.

  • Anthropic HH RLHF data and its Japanese translation
  • FLAN Instruction Tuning data and its Japanese translation

• Model Series

  Variant	Link
  Bilingual 4B MiniGPT4	https://huggingface.co/rinna/bilingual-gpt-neox-4b-minigpt4
  Bilingual 4B PPO	https://huggingface.co/rinna/bilingual-gpt-neox-4b-instruction-ppo
  Bilingual 4B SFT	https://huggingface.co/rinna/bilingual-gpt-neox-4b-instruction-sft
  Bilingual 4B 8K	https://huggingface.co/rinna/bilingual-gpt-neox-4b-8k
  Bilingual 4B	https://huggingface.co/rinna/bilingual-gpt-neox-4b
  Japanese 3.6B PPO	https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-ppo
  Japanese 3.6B SFT-v2	https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-sft-v2
  Japanese 3.6B SFT	https://huggingface.co/rinna/japanese-gpt-neox-3.6b-instruction-sft
  Japanese 3.6B	https://huggingface.co/rinna/japanese-gpt-neox-3.6b

• Contributors

  Tianyu Zhao and Kei Sawada

• Release date

  August 2, 2023

Benchmarking

Our evaluation experiments suggest that the bilingual-gpt-neox-4b-instruction-sft model performs slightly better than the previous Japanese GPT-NeoX 3.6B PPO in Japanese tasks.

• The 4-task average accuracy is based on results of JCommonsenseQA, JNLI, MARC-ja, and JSQuAD.
• The 6-task average accuracy is based on results of JCommonsenseQA, JNLI, MARC-ja, JSQuAD, XWinograd, and JAQKET-v2.

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.
• All the utterances in the input prompt should be separated by a newline \n.

Following is an example to construct input from a conversation.

prompt = [
    {
        "speaker": "ユーザー",
        "text": "Hello, you are an assistant that helps me learn Japanese."
    },
    {
        "speaker": "システム",
        "text": "Sure, what can I do for you?"
    },
    {
        "speaker": "ユーザー",
        "text": "VRはなんですか。"
    }
]
prompt = [
    f"{uttr['speaker']}: {uttr['text']}"
    for uttr in prompt
]
prompt = "\n".join(prompt)
prompt = (
    prompt
    + "\n"
    + "システム: "
)
print(prompt)
"""
ユーザー: Hello, you are an assistant that helps me learn Japanese.
システム: Sure, what can I do for you?
ユーザー: VRはなんですか。
システム:
"""

How to use the model

Notice: Since the model is sensitive to decoding hyper-parameters (e.g. temperature, top_p, top_k, repetition_penalty), it is suggested to explore the best setting for your task.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rinna/bilingual-gpt-neox-4b-instruction-sft", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("rinna/bilingual-gpt-neox-4b-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),
        max_new_tokens=512,
        do_sample=True,
        temperature=1.0,
        top_p=0.85,
        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):])
print(output)
"""VRとはVirtual Realityの略で、仮想現実とも呼ばれます。これは、コンピューターを使用して仮想世界を作り出し、仮想世界上でコンピューターのゲームや仮想世界を体験するための技術です。この技術は、コンピューターやモバイ ルデバイスの進歩によって、2015年以降、ますます普及しています。VRは、ゲームや仮想世界、その他のアプリケー ションなどのさまざまな分野で、コンピューターと人間の相互作用の新しい方法を提供しています。</s>"""

Tokenization

The model uses a sentencepiece-based tokenizer.

• The tokenizer has a vocabulary size of 65,536.
• It uses byte fallback to decompose unknown text pieces into UTF-8 byte pieces to avoid producing <UNK> tokens.
• It can recognize consecutive whitespaces, newlines, and tabs to handle structured texts better.
• We turned off the default behaviour of prepending leading whitespace because it is not beneficial for processing Japanese.
• Specifically, single whitespace is always processed as one token so that any English word won't have a preceding whitespace like in many other tokenizers (e.g. _Hello).
  • This decision trades the English processing efficiency for a unified way to treat whitespaces.
  • It leads to a significantly lower loss of next token prediction on English data because whitespaces are easy to predict.
• Don't forget to set use_fast=False to make the above features function correctly.

How to cite

@misc{rinna-bilingual-gpt-neox-4b-instruction-sft,
    title = {rinna/bilingual-gpt-neox-4b-instruction-sft},
    author = {Zhao, Tianyu and Sawada, Kei},
    url = {https://huggingface.co/rinna/bilingual-gpt-neox-4b-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