Rakutenai 7B Chat

🚀 RakutenAI-7B-chat

RakutenAI-7B-chat是一个系统性的项目，它将最新技术引入到日语大语言模型领域。该模型在日语理解基准测试中取得了最佳成绩，同时在英语测试集上与OpenCalm、Elyza、Youri、Nekomata和Swallow等类似模型相比，也保持着有竞争力的性能。

🚀 快速开始

RakutenAI-7B-chat模型在日语和英语语言处理方面表现出色。若你正在寻找基础模型，可查看 RakutenAI-7B；若你需要指令微调模型，可查看 RakutenAI-7B-instruct。

✨ 主要特性

• 性能卓越：RakutenAI-7B在日语语言理解基准测试中取得最佳成绩，在英语测试集上也有有竞争力的表现。
• 架构先进：采用Mistral模型架构，并基于 Mistral-7B-v0.1 预训练检查点。
• 词汇扩展：将Mistral的词汇表从32k扩展到48k，为日语提供更好的字符与标记比率。

💻 使用示例

基础用法

# With RakutenAI-7B-Chat's custom chat template.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "Rakuten/RakutenAI-7B-chat"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype="auto", device_map="auto")
model.eval()

chat = [
    
    {"role": "system", "content": "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions."},
    {"role": "user", "content": "How to make an authentic Spanish Omelette?"},
]

input_ids = tokenizer.apply_chat_template(chat, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(device=model.device)
tokens = model.generate(
    input_ids,
    max_length=4096,
    do_sample=False,
    num_beams=1,
    pad_token_id=tokenizer.eos_token_id,
)
out = tokenizer.decode(tokens[0][len(input_ids[0]):], skip_special_tokens=True)
print("ASSISTANT:\n" + out)
print()


# Without using custom chat template.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "Rakuten/RakutenAI-7B-chat"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype="auto", device_map="auto")
model.eval()

requests = [
    "「馬が合う」はどう言う意味ですか",
    "How to make an authentic Spanish Omelette?",
]

system_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:"

for req in requests:
    input_req = system_message.format(user_input=req)
    input_ids = tokenizer.encode(input_req, return_tensors="pt").to(device=model.device)
    tokens = model.generate(
        input_ids,
        max_new_tokens=1024,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id,
    )
    out = tokenizer.decode(tokens[0][len(input_ids[0]):], skip_special_tokens=True)
    print("USER:\n" + req)
    print("ASSISTANT:\n" + out)
    print()
    print()

📚 详细文档

模型详情

局限性和偏差

RakutenAI-7B系列模型能够在广泛的主题上生成类似人类的文本。然而，像所有大语言模型一样，它们也有局限性，可能会产生有偏见、不准确或不安全的输出。在与它们交互时，请谨慎并运用判断力。

📄 许可证

本模型采用 Apache License, Version 2.0 许可。

🔧 技术细节

技术报告可在 arXiv 上获取。

📚 引用

如需引用我们在RakutenAI-7B系列模型上的工作，请使用以下格式：

@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}
}

⚠️ 重要提示

该模型可能会产生有偏见、不准确或不安全的输出，请谨慎使用。

💡 使用建议

在使用模型时，可根据具体需求选择合适的版本，如基础模型或指令微调模型。