一个7B参数类似于GPT的模型,使用DiscoPOP在公共可用和合成数据集的混合上微调

21次提交流 2个月前更新

2个月前

246e43cff4fb · 7.1GB

model
gemma
·
8.54B
·
Q5_K_M
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HuggingFaceH4/zephyr-7b-gemma-sft-v0.1在argilla/dpo-mix-7k数据集上的微调版本。

此模型来自论文“使用和针对大型语言模型发现偏置优化算法”

在此处阅读相关博客文章!https://sakana.ai/llm-squared

在此处查看生成此模型的代码库: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,不同之处在于它使用了DiscoPOP,而不是直接使用偏置优化(DPO)。

DiscoPOP是我们发现的偏置优化算法,定义为以下内容

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

以下超参数在训练期间使用
- learning_rate: 5e-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2

框架版本

  • 变压器 4.40.1
  • Pytorch 2.1.2+cu121
  • 数据集 2.19.0
  • 分词器 0.19.1

Zephyr 7B Gemma 模型卡

Zephyr 是一系列经过训练以作为有用助手的语言模型。Zephyr 7B Gemma 是这一系列的第三个模型,它是基于 google/gemma-7b 的微调版本,该版本在公开可获得的数据集和合成数据集的混合体上使用直接偏好优化(DPO)进行训练。您可以通过在 Alignment Handbook 中提供的配方重现此模型的训练。

模型描述

  • 模型类型:在公开可获得的数据集和合成数据集的混合体上微调的 7B 参数 GPT 类似模型。
  • 语言(NLP):主要是英语
  • 许可证:Gemma 使用条款
  • 微调自模型: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 | 版本 | 指标 | 价值 | | 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| |agival_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| |agieval_sat_en | 0 | acc |54.85|± | 3.48| | | |acc_norm|53.40|± | 3.48| |agieval_sat_en_without_passage| 0 | acc |37.38|± | 3.38| | | |acc_norm|33.98|± | 3.31| |agieval_sat_math | 0 | acc |30.91|± | 3.12| | | |acc_norm|28.18|± | 3.04| 平均:34.22% ### GPT4All | 任务 | 版本 | 指标 | 价值 | | 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|77.80|± | 0.97| 平均:66.37% ### TruthfulQA | 任务 | 版本 | 指标 | 价值 | | 标准误差 | |-------------|------:|------|----:|---|-----:| |truthfulqa_mc| 1 | mc1 |35.74|± | 1.68| | | |mc2 |52.19|± | 1.59| 平均:52.19% ### Bigbench | 任务 | 版本 | 指标 | 价值 | | 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|± | 0.00| |bigbench_logical_deduction_five_objects | 0 | multiple_choice_grade|28.40|± | 2.02| |bigbench_logical_deduction_seven_objects | 0 | multiple_choice_grade|19.14|± | 1.49| |bigbench_logical_deduction_three_objects | 0 | multiple_choice_grade|44.67|± | 2.88| |bigbench_movie_recommendation | 0 | multiple_choice_grade|26.80|± | 1.98| |bigbench_navigate | 0 | multiple_choice_grade|50.00|± | 1.58| |bigbench_reasoning_about_colored_objects | 0 | multiple_choice_grade|52.75|± | 1.12| |bigbench_ruin_names | 0 | multiple_choice_grade|33.04|± | 2.22| |bigbench_salient_translation_error_detection | 0 | multiple_choice_grade|33.37|± | 1.49| |bigbench_snarks | 0 | multiple_choice_grade|48.62|± | 3.73| |bigbench_sports_understanding | 0 | multiple_choice_grade|58.11|± | 1.57| |bigbench_temporal_sequences | 0 | multiple_choice_grade|37.20|± | 1.53| |bigbench_tracking_shuffled_objects_five_objects | 0 | multiple_choice_grade|20.08|± | 1.13| |bigbench_tracking_shuffled_objects_seven_objects| 0 | multiple_choice_grade|15.77|± | 0.87| |bigbench_tracking_shuffled_objects_three_objects| 0 | multiple_choice_grade|44.67|± | 2.88| 平均:37.1%

预期用途与限制

该模型最初是在包含 ChatGPT 生成的大量合成对话的 DEITA 10K 数据集上进行微调的。
然后我们进一步将模型与🤗 TRL的 DPOTrainer 对齐,在argilla/dpo-mix-7k 数据集上,该数据集包含7k个由GPT-4排名的提示和模型生成的内容。因此,该模型可用于聊天,您可以检查我们的演示以测试其功能。

以下是如何使用🤗 Transformers中的pipeline()函数运行模型的方法

# 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、风险和局限性

Zephyr 7B Gemma在RLHF阶段尚未根据人类偏好进行安全对齐或像ChatGPT那样部署带有响应过程中过滤的,因此模型可能会生成有问题的输出(尤其是当被提示这样做时)。关于用于训练基模型(google/gemma-7b)的语料库的大小和组成尚未知,但它很可能是包括了Web数据和技术来源(如书籍和代码)的混合。有关示例,请参见StarCoder2模型卡

训练和评估数据

此模型是HuggingFaceH4/zephyr-7b-gemma-sft-v0.1在argilla/dpo-mix-7k数据集上的微调版本。

它在对评估集上实现了以下结果
- 损失:0.4695
- 奖励/选择:-3.3746
- 奖励/拒绝:-4.9715
- 奖励/准确性:0.7188
- 奖励/差异: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

以下超参数在训练期间使用
- learning_rate: 5e-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2

训练结果

训练损失 纪元 步骤 验证损失 奖励/选择 奖励/拒绝 奖励/准确性 奖励/差异 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
  • Datasets 2.14.6
  • Tokenizers 0.15.1

引用信息

如果您在您的工作中发现了这个模型的有用性,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}
}

您还可以考虑引用该模型的创作者

@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

详细结果可在这里找到

指标
平均 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