RakutenAI-7B is a systematic initiative that brings the latest technologies to the world of Japanese LLMs. RakutenAI-7B achieves the best scores on the Japanese language understanding benchmarks while maintaining a competitive performance on the English test sets among similar models such as OpenCalm, Elyza, Youri, Nekomata and Swallow. RakutenAI-7B leverages the Mistral model architecture and is based on Mistral-7B-v0.1 pre-trained checkpoint, exemplifying a successful retrofitting of the pre-trained model weights. Moreover, we extend Mistral's vocabulary from 32k to 48k to offer a better character-per-token rate for Japanese.
An independent evaluation by Kamata et.al. for Nejumi LLMリーダーボード Neo using a weighted average of llm-jp-eval and Japanese MT-bench also confirms the highest performance of instruct/chat versions of RakutenAI-7B.
Usage
python
1from transformers import AutoModelForCausalLM, AutoTokenizer
23model_path ="Rakuten/RakutenAI-7B-instruct"4tokenizer = AutoTokenizer.from_pretrained(model_path)5model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype="auto", device_map="auto")6model.eval()78requests =[9"「馬が合う」はどう言う意味ですか",10"How to make an authentic Spanish Omelette?",11]1213system_message ="A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {user_input} ASSISTANT:"1415for req in requests:16 input_req = system_message.format(user_input=req)17 input_ids = tokenizer.encode(input_req, return_tensors="pt").to(device=model.device)18 tokens = model.generate(19 input_ids,20 max_new_tokens=1024,21 do_sample=True,22 pad_token_id=tokenizer.eos_token_id,23)24 out = tokenizer.decode(tokens[0][len(input_ids[0]):], skip_special_tokens=True)25print("USER:\n"+ req)26print("ASSISTANT:\n"+ out)27print()28print()
Instruction-Tuning Dataset: We fine-tune our foundation model to create RakutenAI-7B-instruct and RakutenAI-7B-chat using a mix of open source and internally hand-crafted datasets. We use train part of the following datasets (CC by-SA License) for instruction-tuned and chat-tuned models:
The suite of RakutenAI-7B models is capable of generating human-like text on a wide range of topics. However, like all LLMs, they have limitations and can produce biased, inaccurate, or unsafe outputs. Please exercise caution and judgement while interacting with them.
Citation
For citing our work on the suite of RakutenAI-7B models, please use:
@misc{rakutengroup2024rakutenai7b,
title={RakutenAI-7B: Extending Large Language Models for Japanese},
author={{Rakuten Group, Inc.} and Aaron Levine and Connie Huang and Chenguang Wang and Eduardo Batista and Ewa Szymanska and Hongyi Ding and Hou Wei Chou and Jean-François Pessiot and Johanes Effendi and Justin Chiu and Kai Torben Ohlhus and Karan Chopra and Keiji Shinzato and Koji Murakami and Lee Xiong and Lei Chen and Maki Kubota and Maksim Tkachenko and Miroku Lee and Naoki Takahashi and Prathyusha Jwalapuram and Ryutaro Tatsushima and Saurabh Jain and Sunil Kumar Yadav and Ting Cai and Wei-Te Chen and Yandi Xia and Yuki Nakayama and Yutaka Higashiyama},
year={2024},
eprint={2403.15484},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
