ELYZA-Thinking-1.0-Qwen-32B

Model Description

ELYZA-Thinking-1.0-Qwen-32B is a reasoning model trained by ELYZA, Inc. Based on Qwen/Qwen2.5-32B-Instruct, this model has been post-trained to enhance its reasoning capabilities in Japanese (Built with Qwen).

During the post-training phase, the model was trained via imitation learning using synthetic data that includes long Chains of Thought (CoT), which were generated through an Monte Carlo Tree Search (MCTS) based algorithm. For more details, please refer to our blog post.

Usage

You can use the model with the Hugging Face Transformers library. The following code is an example of how to use the model for inference.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "elyza/ELYZA-Thinking-1.0-Qwen-32B"
prompt = "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。"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto",
)
model.eval()
messages = [{"role": "user", "content": prompt}]
input_text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
token_ids = tokenizer.encode(
    input_text, add_special_tokens=False, return_tensors="pt"
)
with torch.no_grad():
    output_ids = model.generate(
        token_ids.to(model.device),
        max_new_tokens=8192,
        do_sample=True,
        temperature=0.6,
        top_p=0.95,
    )
output = tokenizer.decode(
    output_ids.tolist()[0][token_ids.size(1):], skip_special_tokens=True
)
print(output)

For deployment, vLLM is recommended to create an OpenAI-Compatible Server.

vllm serve elyza/ELYZA-Thinking-1.0-Qwen-32B \
    --tensor-parallel-size 8 \
    --max-model-len 32768 \
    --enable-reasoning \
    --reasoning-parser deepseek_r1

[!Tip] We recommend setting the temperature between 0.5 and 0.7 and using a top_p value of 0.95 to prevent the model from falling into repetitive loops.

How to Cite

@misc{elyza2025thinking,
    title={elyza/ELYZA-Thinking-1.0-Qwen-32B},
    url={https://huggingface.co/elyza/ELYZA-Thinking-1.0-Qwen-32B},
    author={Masato Hirakawa and Tomoaki Nakamura and Akira Sasaki and Daisuke Oba and Shoetsu Sato},
    year={2025},
}

Citations

@misc{qwen2.5,
    title = {Qwen2.5: A Party of Foundation Models},
    url = {https://qwenlm.github.io/blog/qwen2.5/},
    author = {Qwen Team},
    month = {September},
    year = {2024}
}

@article{qwen2,
      title={Qwen2 Technical Report}, 
      author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
      journal={arXiv preprint arXiv:2407.10671},
      year={2024}
}