bilingual-gpt-neox-4b-instruction-ppo

bilingual-gpt-neox-4b-instruction-ppo

---

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-instruction-sft and has been aligned to serve as an instruction-following conversational agent.

• Model architecture

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

• RLHF

  Following the OpenAI InstructGPT paper, Reinforcement Learning from Human Feedback (RLHF) has been applied to aligning the model's behaviour with input instructions. Particularly, the model has been trained in two stages, i.e. Supervised Fine-Tuning (SFT) and PPO-based Reinforcement Learning (RL).

  • The first SFT stage produces rinna/bilingual-gpt-neox-4b-instruction-sft.
  • The second RL stage produces this model.

• Reinforcement learning

  We used CarperAI/trlx and its implementation of the PPO algorithm for the RL stage.

  The RL data is the subset of the following dataset and has been translated into Japanese.

  • Anthropic HH RLHF data

• 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

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Benchmarking

Our evaluation experiments suggest that the PPO does not particularly improve the model's performance on the Japanese LLM benchmark in comparison with Bilingual GPT-NeoX 4B SFT, but we have seen better conversation experience on the PPO model than its SFT counterpart.

• 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-ppo", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("rinna/bilingual-gpt-neox-4b-instruction-ppo")

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

@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