Ablation 141 A128.dpo.armorm.rp Shisa V2 Llama 3.1 8b

🚀 outputs/ablation-141-a128.dpo.armorm.rp-shisa-v2-llama-3.1-8b模型

该模型是一个基于Transformer架构的语言模型，它通过微调特定基础模型得到，使用了DPO方法进行训练，能在文本生成等任务中展现出良好的性能。

🚀 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="None", 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

• 该模型是 None 的微调版本。
• 使用 TRL 进行训练。
• 采用DPO方法进行优化，相关论文为 Direct Preference Optimization: Your Language Model is Secretly a Reward Model。

🔧 技术细节

训练过程

此模型使用DPO方法进行训练，该方法在论文 Direct Preference Optimization: Your Language Model is Secretly a Reward Model 中被提出。

框架版本

📄 许可证

该模型遵循指定的许可证，具体请查看 license 文件。

📚 引用信息

引用DPO

@inproceedings{rafailov2023direct,
    title        = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
    author       = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
    year         = 2023,
    booktitle    = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
    url          = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
    editor       = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}

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}}
}