一个在公开可用的合成数据集混合上使用DiscoPOP微调的7B参数的类似GPT模型

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2个月前

955a508dd1b0 · 6.0GB

model
gemma
·
8.54B
·
Q4_K_S
template
<|im_start|>system {{ .System }}<|im_end|> <|im_start|>user {{ .Prompt }}<|im_end|> <|im_start|>assistant
license
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params
{"penalize_newline":false,"repeat_penalty":1,"stop":["<|im_start|>","<|im_end|>"]}

README

Zephyr 7B Gemma Logo

DiscoPOP-zephyr-7b-gemma

此模型是在argilla/dpo-mix-7k数据集上对HuggingFaceH4/zephyr-7b-gemma-sft-v0.1进行微调后的版本。

此模型来自论文“Discovering Preference Optimization Algorithms with and for Large Language Models”

在此处阅读关于它的博客文章!

在此处查看生成它的代码库:https://github.com/SakanaAI/DiscoPOP

模型描述

此模型在训练上与HuggingFaceH4/zephyr-7b-gemma-v0.1相同,除了不是使用直接偏好优化(DPO),而是使用DiscoPOP。

DiscoPOP是我们发现偏好优化的算法,其定义如下

def log_ratio_modulated_loss(
    self,
    policy_chosen_logps: torch.FloatTensor,
    policy_rejected_logps: torch.FloatTensor,
    reference_chosen_logps: torch.FloatTensor,
    reference_rejected_logps: torch.FloatTensor,
) -> torch.FloatTensor:
    pi_logratios = policy_chosen_logps - policy_rejected_logps
    ref_logratios = reference_chosen_logps - reference_rejected_logps
    logits = pi_logratios - ref_logratios
    # Modulate the mixing coefficient based on the log ratio magnitudes
    log_ratio_modulation = torch.sigmoid(logits)
    logistic_component = -F.logsigmoid(self.beta * logits)
    exp_component = torch.exp(-self.beta * logits)
    # Blend between logistic and exponential component based on log ratio modulation
    losses = logistic_component * (1 - log_ratio_modulation) + exp_component * log_ratio_modulation
    return losses

训练超参数

以下超参数用于训练期间
- learning_rate: 5e-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- 分布类型:多GPU
- 设备数量:8
- 梯度累积步数:8
- 总训练批次大小:128
- 总评估批次大小:32
- 优化器:Adam,beta值为(0.9,0.999),epsilon为1e-08
- 学习率调度器类型:余弦
- 学习率调度器预热比例:0.1
- 总训练轮数:2

框架版本

  • Transformers 4.40.1
  • Pytorch 2.1.2+cu121
  • Datasets 2.19.0
  • Tokenizers 0.19.1

Zephyr 7B Gemma 模型卡片

Zephyr 是一系列经过训练以作为有帮助助手的语言模型。Zephyr 7B Gemma 是该系列的第三个模型,是使用直接偏好优化 (DPO) 在公开可用和合成的数据集上训练的 google/gemma-7b 的微调版本。您可以通过在 Alignment Handbook 中提供的配方来重新生成该模型的训练。

模型描述

  • 模型类型:一个7B参数类似GPT的模型,在公开可用和合成的数据集上微调。
  • 语言(NLP):主要是英语
  • 许可:Gemma 使用条款
  • 优化自模型:google/gemma-7b

模型来源

性能

模型 MT Bench⬇️ IFEval
zephyr-7b-gemma-v0.1 7.81 28.76
zephyr-7b-beta 7.34 43.81
google/gemma-7b-it 6.38 38.01
模型 AGIEval GPT4All TruthfulQA BigBench 平均 ⬇️
zephyr-7b-beta 37.52 71.77 55.26 39.77 51.08
zephyr-7b-gemma-v0.1 34.22 66.37 52.19 37.10 47.47
mlabonne/Gemmalpaca-7B 21.6 40.87 44.85 30.49 34.45
google/gemma-7b-it 21.33 40.84 41.70 30.25 33.53
AGIEval、GPT4All、TruthfulQA、BigBench的详细信息### AGIEval | 任务 |Version| 指标 |值| |标准误差| |------------------------------|------:|--------|----:|---|-----:| |agieval_aqua_rat | 0|acc |21.65|± | 2.59| | | |acc_norm|25.20|± | 2.73| |agieval_logiqa_en | 0|acc |34.72|± | 1.87| | | |acc_norm|35.94|± | 1.88| |agieval_lsat_ar | 0|acc |19.57|± | 2.62| | | |acc_norm|21.74|± | 2.73| |agieval_lsat_lr | 0|acc |30.59|± | 2.04| | | |acc_norm|32.55|± | 2.08| |agieval_lsat_rc | 0|acc |49.07|± | 3.05| | | |acc_norm|42.75|± | 3.02| |agival_sat_en | 0|acc |54.85|± | 3.48| | | |acc_norm|53.40|± | 3.48| |agival_sat_en_without_passage| 0|acc |37.38|± | 3.38| | | |acc_norm|33.98|± | 3.31| |agival_sat_math | 0|acc |30.91|± | 3.12| | | |acc_norm|28.18|± | 3.04| 平均: 34.22% ### GPT4All | 任务 |Version| 指标 |值| |标准误差| |-------------|------:|--------|----:|---|-----:| |arc_challenge| 0|acc |49.15|± | 1.46| | | |acc_norm|52.47|± | 1.46| |arc_easy | 0|acc |77.44|± | 0.86| | | |acc_norm|74.75|± | 0.89| |boolq | 1|acc |79.69|± | 0.70| | | |acc_norm|78.00|± | 0.41| |hellaswag | 0|acc |60.59|± | 0.49| | | |acc_norm|78.00|± | 0.41| |openbookqa | 0|acc |29.20|± | 2.04| | | |acc_norm|37.80|± | 2.17| |piqa | 0|acc |76.82|± | 0.98| | | |acc_norm|77.80|± | 0.97| |winogrande | 0|acc |64.09|± | 1.35| | | |acc_norm|64.09|± | 1.35| 平均: 66.37% ### TruthfulQA | 任务 |Version| 指标|值| |标准误差| |-------------|------:|------|----:|---|-----:| |truthfulqa_mc| 1|mc1 |35.74|± | 1.68| | | |mc2 |52.19|± | 1.59| 平均: 52.19% ### Bigbench | 任务 |Version| 指标 |值| |标准误差| |------------------------------------------------|------:|---------------------|----:|---|-----:| |bigbench_causal_judgement | 0|multiple_choice_grade|53.68|± | 3.63| |bigbench_date_understanding | 0|multiple_choice_grade|59.89|± | 2.55| |bigbench_disambiguation_qa | 0|multiple_choice_grade|30.23|± | 2.86| |bigbench_geometric_shapes | 0|multiple_choice_grade|11.42|± | 1.68| | | |exact_str_match | 0.00|&#...

预期用途及限制

该模型最初在包含ChatGPT生成的多种合成对话的DEITA 10K数据集上进行微调。
然后我们在包含由GPT-4评分的7k个提示和模型补全的🤗 TRL的 DPOTrainer上进一步调整了模型,即argilla/dpo-mix-7k数据集。因此,该模型可用于聊天,您可以查看我们的演示以测试其功能。

以下是您可以使用🤗 Transformers中的pipeline()函数运行模型的方式

# pip install transformers>=4.38.2
# pip install accelerate

import torch
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="HuggingFaceH4/zephyr-7b-gemma-v0.1",
    device_map="auto",
    torch_dtype=torch.bfloat16,
)
messages = [
    {
        "role": "system",
        "content": "",  # Model not yet trained for follow this
    },
    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
outputs = pipe(
    messages,
    max_new_tokens=128,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95,
    stop_sequence="<|im_end|>",
)
print(outputs[0]["generated_text"][-1]["content"])
# It is not possible for a human to eat a helicopter in one sitting, as a
# helicopter is a large and inedible machine. Helicopters are made of metal,
# plastic, and other materials that are not meant to be consumed by humans.
# Eating a helicopter would be extremely dangerous and would likely cause
# serious health problems, including choking, suffocation, and poisoning. It is
# important to only eat food that is safe and intended for human consumption.

偏差、风险和限制

Zephyr 7B Gemma在RLHF阶段尚未与人类安全偏好一致调整,并且在部署时没有像ChatGPT那样的反馈回路来过滤响应,因此模型可能会生成有问题的输出(尤其是在被提示时)。此外,不清楚用于训练基础模型(google/gemma-7b)的语料库的大小和组成,但它很可能包括Web数据和技术来源,如书籍和代码的混合。参见StarCoder2模型卡片了解此类示例。

训练和评估数据

此模型是在argilla/dpo-mix-7k数据集上对HuggingFaceH4/zephyr-7b-gemma-sft-v0.1进行微调后的版本。

在评估集上实现以下结果
- 损失:0.4695
- 奖励/选择:-3.3746
- 奖励/拒绝:-4.9715
- 奖励/准确度:0.7188
- 奖励/边缘:1.5970
- Logps/拒绝:-459.4853
- Logps/选择:-429.9115
- Logits/拒绝:86.4684
- Logits/选择:92.8200

训练超参数

以下超参数用于训练期间
- learning_rate: 5e-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- 分布类型:多GPU
- 设备数量:8
- 梯度累积步数:8
- 总训练批次大小:128
- 总评估批次大小:32
- 优化器:Adam,beta值为(0.9,0.999),epsilon为1e-08
- 学习率调度器类型:余弦
- 学习率调度器预热比例:0.1
- 总训练轮数:2

训练结果

训练损失 epoch 步骤 验证损失 奖励/选择 奖励/拒绝 奖励/准确度 奖励/边缘 Logps/拒绝 Logps/选择 Logits/拒绝 Logits/选择
0.1923 1.9 100 0.4736 -3.4575 -4.9556 0.75 1.4980 -459.1662 -431.5707 86.3863 92.7360

框架版本

  • Transformers 4.39.0.dev0
  • Pytorch 2.1.2+cu121
  • 数据集 2.14.6
  • 分词器 0.15.1

引用信息

如果您在工作中发现此模型有用,请考虑引用Zephyr技术报告

@misc{tunstall2023zephyr,
      title={Zephyr: Direct Distillation of LM Alignment}, 
      author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf},
      year={2023},
      eprint={2310.16944},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

您还可能希望引用此模型的创作者

@misc{zephyr_7b_gemma,
  author = {Lewis Tunstall and Philipp Schmid},
  title = {Zephyr 7B Gemma},
  year = {2024},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  howpublished = {\url{https://hugging-face.cn/HuggingFaceH4/zephyr-7b-gemma-v0.1}}
}

开放LLM排行榜评估结果

详细信息请查看此处

指标
Avg. 62.41
AI2 Reasoning Challenge(25-Shot) 58.45
HellaSwag(10-Shot) 83.48
MMLU(5-Shot) 60.68
TruthfulQA(0-Shot) 52.07
Winogrande(5-shot) 74.19
GSM8k(5-shot) 45.56