🚀 Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fierce_placid_whale

本模型是基于Transformer架构的微调语言模型，在问答、文本生成等自然语言处理任务中表现出色。它基于预训练模型进一步优化，能更精准地理解和生成文本。

🚀 快速开始

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="gangchen/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-fierce_placid_whale", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

✨ 主要特性

• 本模型是 Gensyn/Qwen2.5-0.5B-Instruct 的微调版本。
• 使用 TRL 进行训练。

🔧 技术细节

训练方法

本模型使用GRPO方法进行训练，该方法在论文 DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models 中被提出。

框架版本

• TRL: 0.15.2
• Transformers: 4.51.3
• Pytorch: 2.5.1
• Datasets: 3.5.0
• Tokenizers: 0.21.1

📄 许可证

本模型遵循 license 许可证。

📚 详细文档

模型信息

引用信息

引用GRPO

@article{zhihong2024deepseekmath,
    title        = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
    author       = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
    year         = 2024,
    eprint       = {arXiv:2402.03300},
}

引用TRL

@misc{vonwerra2022trl,
	title        = {{TRL: Transformer Reinforcement Learning}},
	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
	year         = 2020,
	journal      = {GitHub repository},
	publisher    = {GitHub},
	howpublished = {\url{https://github.com/huggingface/trl}}
}