metadata
thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
license: llama2
language:
- ja
- en
inference: false
datasets:
- databricks/databricks-dolly-15k
- kunishou/databricks-dolly-15k-ja
- izumi-lab/llm-japanese-dataset
rinna/youri-7b-chat
Overview
The model is the instruction-tuned version of rinna/youri-7b. It adopts a chat-style input format.
Model architecture
A 32-layer, 4096-hidden-size transformer-based language model. Refer to the llama2 paper for architecture details.
Fine-tuning
The fine-tuning data is the subset of the following datasets.
- Databricks Dolly data
- Japanese Databricks Dolly data
- Anthropic HH RLHF data and its Japanese translation
- FLAN Instruction Tuning data and its Japanese translation
- Izumi lab LLM Japanese dataset
- The following sections are used
- alt
- aozora-txt
- CourseraParallel
- ParaNatCom
- Tab-delimited_Bilingual_Sentence_Pairs
- tanaka-corpus
- wikinews
- wordnet
- yasashi-japanese
- The remaining sections contain commonly used evaluation corpora so they are skipped to prevent data leak.
- The following sections are used
Authors
Benchmarking
Please refer to rinna's LM benchmark page.
How to use the model
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("rinna/youri-7b-chat")
model = AutoModelForCausalLM.from_pretrained("rinna/youri-7b-chat")
if torch.cuda.is_available():
model = model.to("cuda")
instruction = "次の日本語を英語に翻訳してください。"
input = "自然言語による指示に基づきタスクが解けるよう学習させることを Instruction tuning と呼びます。"
context = [
{
"speaker": "設定",
"text": instruction
},
{
"speaker": "ユーザー",
"text": input
}
]
prompt = [
f"{uttr['speaker']}: {uttr['text']}"
for uttr in context
]
prompt = "\n".join(prompt)
prompt = (
prompt
+ "\n"
+ "システム: "
)
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=200,
do_sample=True,
temperature=0.5,
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)
"""
設定: 次の日本語を英語に翻訳してください。
ユーザー: 自然言語による指示に基づきタスクが解けるよう学習させることを Instruction tuning と呼びます。
システム: Learning to solve tasks based on natural language instructions is called instruction tuning.</s>
"""
output = output[len(prompt):-len("</s>")].strip()
input = "大規模言語モデル（だいきぼげんごモデル、英: large language model、LLM）は、多数のパラメータ（数千万から数十億）を持つ人工ニューラルネットワークで構成されるコンピュータ言語モデルで、膨大なラベルなしテキストを使用して自己教師あり学習または半教師あり学習によって訓練が行われる。"
context.extend([
{
"speaker": "システム",
"text": output
},
{
"speaker": "ユーザー",
"text": input
}
])
prompt = [
f"{uttr['speaker']}: {uttr['text']}"
for uttr in context
]
prompt = "\n".join(prompt)
prompt = (
prompt
+ "\n"
+ "システム: "
)
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=200,
do_sample=True,
temperature=0.5,
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)
"""
設定: 次の日本語を英語に翻訳してください。
ユーザー: 自然言語による指示に基づきタスクが解けるよう学習させることを Instruction tuning と呼びます。
システム: Learning to solve tasks based on natural language instructions is called instruction tuning.
ユーザー: 大規模言語モデル（だいきぼげんごモデル、英: large language model、LLM）は、多数のパラメータ（数千万から数十億）を持つ人工ニューラルネットワークで構成されるコンピュータ言語モデルで、膨大なラベルなしテ キストを使用して自己教師あり学習または半教師あり学習によって訓練が行われる。
システム: Large language models (LLMs) are computer language models consisting of a deep artificial neural network with millions to billions of parameters that are trained by self-supervised learning or semi-supervised learning using vast unlabeled text corpora.</s>
"""
Tokenization
The model uses the original llama-2 tokenizer.
How to cite
@misc{RinnaYouri7bChat,
url={https://huggingface.co./rinna/youri-7b-chat},
title={rinna/youri-7b-chat},
author={Zhao, Tianyu and Sawada, Kei}
}