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

本模型是基于Transformer架构的微调模型，在问答和文本生成任务上表现出色，为用户提供更精准、高效的语言交互体验。

🚀 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="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-feathered_giant_ostrich", 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

• 微调优化：此模型是Gensyn/Qwen2.5-0.5B-Instruct的微调版本，经过优化以适应特定任务。
• 训练框架：使用TRL进行训练，提升了模型的性能和适应性。

📦 安装指南

文档未提供安装步骤，暂不展示。

💻 使用示例

基础用法

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="chinna6/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-feathered_giant_ostrich", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

高级用法

文档未提供高级用法代码示例，暂不展示。

📚 详细文档

训练过程

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: 4.48.2
• Pytorch: 2.5.1
• Datasets: 3.6.0
• Tokenizers: 0.21.1

引用信息

引用GRPO方法时，请使用以下格式：

@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框架时，请使用以下格式：

@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}}
}

📄 许可证

本模型遵循指定的许可证。具体许可证信息请参考 license 文件。