在 princeton-nlp/gemma2-ultrafeedback-armorm 上基于 google/gemma-2-9b-it 进行了微调,采用 SimPO 目标。

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model
gemma2
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9.24B
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Q3_K_S
license
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template
<start_of_turn>user {{ if .System }}{{ .System }} {{ end }}{{ .Prompt }}<end_of_turn> <start_of_turn>model {{ .Response }}<end_of_turn>
params
{"num_ctx":4096,"num_predict":4096,"repeat_penalty":1,"stop":["<start_of_turn>","<end_of_turn>"]}

读我

  • 使用 i-matrix 针对calibration_datav3.txt 进行量化
  • 将 Saftensors 转换为 fp32

gemma-2-9b-it-SimPO 模型卡

SimPO (简单偏好优化) 是一个离线偏好优化算法,用于增强有偏好优化数据集的大语言模型 (LLM) 的训练。SimPO 将奖励函数与生成似然性对齐,消除了对参考模型的需求,并引入目标奖励边距以提高性能。请参阅我们的 预印本GitHub 仓库 以获取更多信息。

模型详情

模型描述

我们使用 SimPO 目标在 princeton-nlp/gemma2-ultrafeedback-armorm 上对 google/gemma-2-9b-it 进行了微调。

  • 开发者:Yu Meng, Mengzhou Xia, Danqi Chen
  • 模型类型:因果关系语言模型
  • 许可证:gemma
  • 微调自模型: google/gemma-2-9b-it

模型源

如何开始使用模型

import torch
from transformers import pipeline

model_id = "princeton-nlp/gemma-2-9b-it-SimPO"

generator = pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cuda",
)
outputs = generator([{"role": "user", "content": "What's the difference between llamas and alpacas?"}], do_sample=False, max_new_tokens=200)
print(outputs[0]['generated_text'])

训练细节

训练数据

我们将 princeton-nlp/gemma2-ultrafeedback-armorm 作为偏好优化数据集。

训练超参数

使用的超参数可以在 训练脚本 中找到。

速度、大小、时间

google/gemma-2-9b-it上对princeton-nlp/gemma2-ultrafeedback-armorm进行微调,在8xH100 GPU上大约需要100分钟完成。

评估结果

模型 AE2 LC AE2 WR AE2 长度 AH AH 长度 GSM GSM 长度 MMLU MMLU 长度
google/gemma-2-9b-it 51.1 38.1 1571 40.8 545 87.4 395 72.7 515
princeton-nlp/gemma-2-9b-it-DPO 67.8 65.4 2016 58.9 717 88.5 392 72.2 624
princeton-nlp/gemma-2-9b-it-SimPO 72.4 65.9 1833 59.1 693 88.0 341 72.2 441

技术规格

模型架构和目标

该模型架构基于google/gemma-2-9b-it。我们使用了我们在预印本中提出的SimPO训练目标。

硬件

我们使用了8xH100 GPU进行模型训练。

软件

使用alignment-handbook库进行训练。

引用

gemma模型

@article{gemma_2024,
    title={Gemma},
    url={https://www.kaggle.com/m/3301},
    DOI={10.34740/KAGGLE/M/3301},
    publisher={Kaggle},
    author={Gemma Team},
    year={2024}
}

SimPO论文

@article{meng2024simpo,
  title={{SimPO}: Simple preference optimization with a reference-free reward},
  author={Meng, Yu and Xia, Mengzhou and Chen, Danqi},
  journal={arXiv preprint arXiv:2405.14734},
  year={2024}
}

UltraFeedback论文

@article{cui2023ultrafeedback,
  title={{UltraFeedback}: Boosting language models with high-quality feedback},
  author={Cui, Ganqu and Yuan, Lifan and Ding, Ning and Yao, Guanming and Zhu, Wei and Ni, Yuan and Xie, Guotong and Liu, Zhiyuan and Sun, Maosong},
  journal={arXiv preprint arXiv:2310.01377},
  year={2023}
}

ArmoRM论文

@article{wang2024interpretable,
  title={Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts},
  author={Wang, Haoxiang and Xiong, Wei and Xie, Tengyang and Zhao, Han and Zhang, Tong},
  journal={arXiv preprint arXiv:2406.12845},
  year={2024}
}