File size: 5,970 Bytes
49e410f 3d06736 49e410f 3d06736 49e410f 3d06736 e6d32e6 3d06736 e6d32e6 3d06736 e1d570e 3d06736 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 |
---
language: ja
thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
tags:
- ja
- gpt_neox
- text-generation
- lm
- nlp
license: mit
datasets:
- Anthropic/hh-rlhf
- stanfordnlp/SHP
inference: false
---
# 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`](https://huggingface.co./rinna/japanese-gpt-neox-3.6b) and has been finetuned to serve as a 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](https://huggingface.co./datasets/Anthropic/hh-rlhf)
* [FLAN Instruction Tuning data](https://github.com/google-research/FLAN)
* [Stanford Human Preferences Dataset](https://huggingface.co./datasets/stanfordnlp/SHP)
The data will **not** be released.
* **Authors**
[Tianyu Zhao](https://huggingface.co./tianyuz) and [Kei Sawada](https://huggingface.co./keisawada)
# 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.
~~~python
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
~~~~python
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](https://github.com/google/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>'
~~~
# Licenese
[The MIT license](https://opensource.org/licenses/MIT)
|