ELYZA Japanese Llama 2 13b Fast Instruct

🚀 ELYZA日本语Llama-2-13b快速指令模型

ELYZA日本语Llama-2-13b是基于Llama 2，为扩展日语能力而进行额外预训练的模型，能有效处理日语相关任务，提升日语交互体验。

🚀 快速开始

模型描述

ELYZA日本语Llama-2-13b 是在Llama 2基础上，为扩展日语能力而进行额外预训练的模型。详细信息请参考 博客文章。

💻 使用示例

基础用法

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
DEFAULT_SYSTEM_PROMPT = "あなたは誠実で優秀な日本人のアシスタントです。"
text = "仕事の熱意を取り戻すためのアイデアを5つ挙げてください。"

model_name = "elyza/ELYZA-japanese-Llama-2-13b-fast-instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    use_cache=True,
    device_map="auto",
    low_cpu_mem_usage=True,
)
model.eval()

prompt = "{bos_token}{b_inst} {system}{prompt} {e_inst} ".format(
    bos_token=tokenizer.bos_token,
    b_inst=B_INST,
    system=f"{B_SYS}{DEFAULT_SYSTEM_PROMPT}{E_SYS}",
    prompt=text,
    e_inst=E_INST,
)
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=256,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )
output = tokenizer.decode(output_ids.tolist()[0][token_ids.size(1) :], skip_special_tokens=True)
print(output)

📚 详细文档

ELYZA日本语Llama-2-13b系列模型

开发人员

• 佐佐木晃
• 平川正人
• 堀江慎太郎
• 中村智晃
• 山姆·帕萨利亚
• 大庭大辅（实习生）

📄 许可证

Llama 2遵循LLAMA 2社区许可证，版权所有 (c) Meta Platforms, Inc. 保留所有权利。

📚 引用方式

@misc{elyzallama2023, 
      title={ELYZA-japanese-Llama-2-13b}, 
      url={https://huggingface.co/elyza/ELYZA-japanese-Llama-2-13b}, 
      author={Akira Sasaki and Masato Hirakawa and Shintaro Horie and Tomoaki Nakamura and Sam Passaglia and Daisuke Oba},
      year={2023},
}

相关引用

@misc{touvron2023llama,
      title={Llama 2: Open Foundation and Fine-Tuned Chat Models}, 
      author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
      year={2023},
      eprint={2307.09288},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}