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metadata
tags:
  - not-for-all-audiences
license: mit
datasets:
  - HuggingFaceH4/ultrachat_200k
  - HuggingFaceH4/ultrafeedback_binarized
language:
  - en
base_model: mistralai/Mistral-7B-v0.1
widget:
  - example_title: Pirate!
    messages:
      - role: system
        content: You are a pirate chatbot who always responds with Arr!
      - role: user
        content: There's a llama on my lawn, how can I get rid of him?
    output:
      text: >-
        Arr! 'Tis a puzzlin' matter, me hearty! A llama on yer lawn be a rare
        sight, but I've got a plan that might help ye get rid of 'im. Ye'll need
        to gather some carrots and hay, and then lure the llama away with the
        promise of a tasty treat. Once he's gone, ye can clean up yer lawn and
        enjoy the peace and quiet once again. But beware, me hearty, for there
        may be more llamas where that one came from! Arr!
pipeline_tag: text-generation
model-index:
  - name: zephyr-7b-beta
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            name: normalized accuracy
            value: 62.03071672354948
        source:
          name: Open LLM Leaderboard
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            name: normalized accuracy
            value: 84.35570603465445
        source:
          name: Open LLM Leaderboard
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Drop (3-Shot)
          type: drop
          split: validation
          args:
            num_few_shot: 3
        metrics:
          - type: f1
            name: f1 score
            value: 9.66243708053691
        source:
          name: Open LLM Leaderboard
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 57.44916942762855
        source:
          name: Open LLM Leaderboard
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            name: accuracy
            value: 12.736921910538287
        source:
          name: Open LLM Leaderboard
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            name: accuracy
            value: 61.07
        source:
          name: Open LLM Leaderboard
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            name: accuracy
            value: 77.7426992896606
        source:
          name: Open LLM Leaderboard
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AlpacaEval
          type: tatsu-lab/alpaca_eval
        metrics:
          - type: unknown
            name: win rate
            value: 0.906
        source:
          url: https://tatsu-lab.github.io/alpaca_eval/
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MT-Bench
          type: unknown
        metrics:
          - type: unknown
            name: score
            value: 7.34
        source:
          url: https://huggingface.co/spaces/lmsys/mt-bench
Zephyr Logo

Model Card for Zephyr 7B β

Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-β is the second model in the series, and is a fine-tuned version of mistralai/Mistral-7B-v0.1 that was trained on on a mix of publicly available, synthetic datasets using Direct Preference Optimization (DPO). We found that removing the in-built alignment of these datasets boosted performance on MT Bench and made the model more helpful. However, this means that model is likely to generate problematic text when prompted to do so. You can find more details in the technical report.

Model description

  • Model type: A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
  • Language(s) (NLP): Primarily English
  • License: MIT
  • Finetuned from model: mistralai/Mistral-7B-v0.1

Model Sources

Performance

At the time of release, Zephyr-7B-β is the highest ranked 7B chat model on the MT-Bench and AlpacaEval benchmarks:

Model Size Alignment MT-Bench (score) AlpacaEval (win rate %)
StableLM-Tuned-α 7B dSFT 2.75 -
MPT-Chat 7B dSFT 5.42 -
Xwin-LMv0.1 7B dPPO 6.19 87.83
Mistral-Instructv0.1 7B - 6.84 -
Zephyr-7b-α 7B dDPO 6.88 -
Zephyr-7b-β 🪁 7B dDPO 7.34 90.60
Falcon-Instruct 40B dSFT 5.17 45.71
Guanaco 65B SFT 6.41 71.80
Llama2-Chat 70B RLHF 6.86 92.66
Vicuna v1.3 33B dSFT 7.12 88.99
WizardLM v1.0 70B dSFT 7.71 -
Xwin-LM v0.1 70B dPPO - 95.57
GPT-3.5-turbo - RLHF 7.94 89.37
Claude 2 - RLHF 8.06 91.36
GPT-4 - RLHF 8.99 95.28

In particular, on several categories of MT-Bench, Zephyr-7B-β has strong performance compared to larger open models like Llama2-Chat-70B:

image/png

However, on more complex tasks like coding and mathematics, Zephyr-7B-β lags behind proprietary models and more research is needed to close the gap.

Intended uses & limitations

The model was initially fine-tuned on a filtered and preprocessed of the UltraChat dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. We then further aligned the model with 🤗 TRL's DPOTrainer on the openbmb/UltraFeedback dataset, which contains 64k prompts and model completions that are ranked by GPT-4. As a result, the model can be used for chat and you can check out our demo to test its capabilities.

You can find the datasets used for training Zephyr-7B-β here

Here's how you can run the model using the pipeline() function from 🤗 Transformers:

# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="HuggingFaceH4/zephyr-7b-beta", torch_dtype=torch.bfloat16, device_map="auto")

# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
    {
        "role": "system",
        "content": "You are a friendly chatbot who always responds in the style of a pirate",
    },
    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
# <|system|>
# You are a friendly chatbot who always responds in the style of a pirate.</s>
# <|user|>
# How many helicopters can a human eat in one sitting?</s>
# <|assistant|>
# Ah, me hearty matey! But yer question be a puzzler! A human cannot eat a helicopter in one sitting, as helicopters are not edible. They be made of metal, plastic, and other materials, not food!

Bias, Risks, and Limitations

Zephyr-7B-β has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition of the corpus was used to train the base model (mistralai/Mistral-7B-v0.1), however it is likely to have included a mix of Web data and technical sources like books and code. See the Falcon 180B model card for an example of this.

Training and evaluation data

During DPO training, this model achieves the following results on the evaluation set:

  • Loss: 0.7496
  • Rewards/chosen: -4.5221
  • Rewards/rejected: -8.3184
  • Rewards/accuracies: 0.7812
  • Rewards/margins: 3.7963
  • Logps/rejected: -340.1541
  • Logps/chosen: -299.4561
  • Logits/rejected: -2.3081
  • Logits/chosen: -2.3531

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 5e-07
  • train_batch_size: 2
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 16
  • total_train_batch_size: 32
  • total_eval_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • num_epochs: 3.0

Training results

The table below shows the full set of DPO training metrics:

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.6284 0.05 100 0.6098 0.0425 -0.1872 0.7344 0.2297 -258.8416 -253.8099 -2.7976 -2.8234
0.4908 0.1 200 0.5426 -0.0279 -0.6842 0.75 0.6563 -263.8124 -254.5145 -2.7719 -2.7960
0.5264 0.15 300 0.5324 0.0414 -0.9793 0.7656 1.0207 -266.7627 -253.8209 -2.7892 -2.8122
0.5536 0.21 400 0.4957 -0.0185 -1.5276 0.7969 1.5091 -272.2460 -254.4203 -2.8542 -2.8764
0.5362 0.26 500 0.5031 -0.2630 -1.5917 0.7812 1.3287 -272.8869 -256.8653 -2.8702 -2.8958
0.5966 0.31 600 0.5963 -0.2993 -1.6491 0.7812 1.3499 -273.4614 -257.2279 -2.8778 -2.8986
0.5014 0.36 700 0.5382 -0.2859 -1.4750 0.75 1.1891 -271.7204 -257.0942 -2.7659 -2.7869
0.5334 0.41 800 0.5677 -0.4289 -1.8968 0.7969 1.4679 -275.9378 -258.5242 -2.7053 -2.7265
0.5251 0.46 900 0.5772 -0.2116 -1.3107 0.7344 1.0991 -270.0768 -256.3507 -2.8463 -2.8662
0.5205 0.52 1000 0.5262 -0.3792 -1.8585 0.7188 1.4793 -275.5552 -258.0276 -2.7893 -2.7979
0.5094 0.57 1100 0.5433 -0.6279 -1.9368 0.7969 1.3089 -276.3377 -260.5136 -2.7453 -2.7536
0.5837 0.62 1200 0.5349 -0.3780 -1.9584 0.7656 1.5804 -276.5542 -258.0154 -2.7643 -2.7756
0.5214 0.67 1300 0.5732 -1.0055 -2.2306 0.7656 1.2251 -279.2761 -264.2903 -2.6986 -2.7113
0.6914 0.72 1400 0.5137 -0.6912 -2.1775 0.7969 1.4863 -278.7448 -261.1467 -2.7166 -2.7275
0.4655 0.77 1500 0.5090 -0.7987 -2.2930 0.7031 1.4943 -279.8999 -262.2220 -2.6651 -2.6838
0.5731 0.83 1600 0.5312 -0.8253 -2.3520 0.7812 1.5268 -280.4902 -262.4876 -2.6543 -2.6728
0.5233 0.88 1700 0.5206 -0.4573 -2.0951 0.7812 1.6377 -277.9205 -258.8084 -2.6870 -2.7097
0.5593 0.93 1800 0.5231 -0.5508 -2.2000 0.7969 1.6492 -278.9703 -259.7433 -2.6221 -2.6519
0.4967 0.98 1900 0.5290 -0.5340 -1.9570 0.8281 1.4230 -276.5395 -259.5749 -2.6564 -2.6878
0.0921 1.03 2000 0.5368 -1.1376 -3.1615 0.7812 2.0239 -288.5854 -265.6111 -2.6040 -2.6345
0.0733 1.08 2100 0.5453 -1.1045 -3.4451 0.7656 2.3406 -291.4208 -265.2799 -2.6289 -2.6595
0.0972 1.14 2200 0.5571 -1.6915 -3.9823 0.8125 2.2908 -296.7934 -271.1505 -2.6471 -2.6709
0.1058 1.19 2300 0.5789 -1.0621 -3.8941 0.7969 2.8319 -295.9106 -264.8563 -2.5527 -2.5798
0.2423 1.24 2400 0.5455 -1.1963 -3.5590 0.7812 2.3627 -292.5599 -266.1981 -2.5414 -2.5784
0.1177 1.29 2500 0.5889 -1.8141 -4.3942 0.7969 2.5801 -300.9120 -272.3761 -2.4802 -2.5189
0.1213 1.34 2600 0.5683 -1.4608 -3.8420 0.8125 2.3812 -295.3901 -268.8436 -2.4774 -2.5207
0.0889 1.39 2700 0.5890 -1.6007 -3.7337 0.7812 2.1330 -294.3068 -270.2423 -2.4123 -2.4522
0.0995 1.45 2800 0.6073 -1.5519 -3.8362 0.8281 2.2843 -295.3315 -269.7538 -2.4685 -2.5050
0.1145 1.5 2900 0.5790 -1.7939 -4.2876 0.8438 2.4937 -299.8461 -272.1744 -2.4272 -2.4674
0.0644 1.55 3000 0.5735 -1.7285 -4.2051 0.8125 2.4766 -299.0209 -271.5201 -2.4193 -2.4574
0.0798 1.6 3100 0.5537 -1.7226 -4.2850 0.8438 2.5624 -299.8200 -271.4610 -2.5367 -2.5696
0.1013 1.65 3200 0.5575 -1.5715 -3.9813 0.875 2.4098 -296.7825 -269.9498 -2.4926 -2.5267
0.1254 1.7 3300 0.5905 -1.6412 -4.4703 0.8594 2.8291 -301.6730 -270.6473 -2.5017 -2.5340
0.085 1.76 3400 0.6133 -1.9159 -4.6760 0.8438 2.7601 -303.7296 -273.3941 -2.4614 -2.4960
0.065 1.81 3500 0.6074 -1.8237 -4.3525 0.8594 2.5288 -300.4951 -272.4724 -2.4597 -2.5004
0.0755 1.86 3600 0.5836 -1.9252 -4.4005 0.8125 2.4753 -300.9748 -273.4872 -2.4327 -2.4716
0.0746 1.91 3700 0.5789 -1.9280 -4.4906 0.8125 2.5626 -301.8762 -273.5149 -2.4686 -2.5115
0.1348 1.96 3800 0.6015 -1.8658 -4.2428 0.8281 2.3769 -299.3976 -272.8936 -2.4943 -2.5393
0.0217 2.01 3900 0.6122 -2.3335 -4.9229 0.8281 2.5894 -306.1988 -277.5699 -2.4841 -2.5272
0.0219 2.07 4000 0.6522 -2.9890 -6.0164 0.8281 3.0274 -317.1334 -284.1248 -2.4105 -2.4545
0.0119 2.12 4100 0.6922 -3.4777 -6.6749 0.7969 3.1972 -323.7187 -289.0121 -2.4272 -2.4699
0.0153 2.17 4200 0.6993 -3.2406 -6.6775 0.7969 3.4369 -323.7453 -286.6413 -2.4047 -2.4465
0.011 2.22 4300 0.7178 -3.7991 -7.4397 0.7656 3.6406 -331.3667 -292.2260 -2.3843 -2.4290
0.0072 2.27 4400 0.6840 -3.3269 -6.8021 0.8125 3.4752 -324.9908 -287.5042 -2.4095 -2.4536
0.0197 2.32 4500 0.7013 -3.6890 -7.3014 0.8125 3.6124 -329.9841 -291.1250 -2.4118 -2.4543
0.0182 2.37 4600 0.7476 -3.8994 -7.5366 0.8281 3.6372 -332.3356 -293.2291 -2.4163 -2.4565
0.0125 2.43 4700 0.7199 -4.0560 -7.5765 0.8438 3.5204 -332.7345 -294.7952 -2.3699 -2.4100
0.0082 2.48 4800 0.7048 -3.6613 -7.1356 0.875 3.4743 -328.3255 -290.8477 -2.3925 -2.4303
0.0118 2.53 4900 0.6976 -3.7908 -7.3152 0.8125 3.5244 -330.1224 -292.1431 -2.3633 -2.4047
0.0118 2.58 5000 0.7198 -3.9049 -7.5557 0.8281 3.6508 -332.5271 -293.2844 -2.3764 -2.4194
0.006 2.63 5100 0.7506 -4.2118 -7.9149 0.8125 3.7032 -336.1194 -296.3530 -2.3407 -2.3860
0.0143 2.68 5200 0.7408 -4.2433 -7.9802 0.8125 3.7369 -336.7721 -296.6682 -2.3509 -2.3946
0.0057 2.74 5300 0.7552 -4.3392 -8.0831 0.7969 3.7439 -337.8013 -297.6275 -2.3388 -2.3842
0.0138 2.79 5400 0.7404 -4.2395 -7.9762 0.8125 3.7367 -336.7322 -296.6304 -2.3286 -2.3737
0.0079 2.84 5500 0.7525 -4.4466 -8.2196 0.7812 3.7731 -339.1662 -298.7007 -2.3200 -2.3641
0.0077 2.89 5600 0.7520 -4.5586 -8.3485 0.7969 3.7899 -340.4545 -299.8206 -2.3078 -2.3517
0.0094 2.94 5700 0.7527 -4.5542 -8.3509 0.7812 3.7967 -340.4790 -299.7773 -2.3062 -2.3510
0.0054 2.99 5800 0.7520 -4.5169 -8.3079 0.7812 3.7911 -340.0493 -299.4038 -2.3081 -2.3530

Framework versions

  • Transformers 4.35.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.12.0
  • Tokenizers 0.14.0

Citation

If you find Zephyr-7B-β is useful in your work, please cite it with:

@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}
}

If you use the UltraChat or UltraFeedback datasets, please cite the original works:

@misc{ding2023enhancing,
      title={Enhancing Chat Language Models by Scaling High-quality Instructional Conversations}, 
      author={Ning Ding and Yulin Chen and Bokai Xu and Yujia Qin and Zhi Zheng and Shengding Hu and Zhiyuan Liu and Maosong Sun and Bowen Zhou},
      year={2023},
      eprint={2305.14233},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

@misc{cui2023ultrafeedback,
      title={UltraFeedback: Boosting Language Models with High-quality Feedback}, 
      author={Ganqu Cui and Lifan Yuan and Ning Ding and Guanming Yao and Wei Zhu and Yuan Ni and Guotong Xie and Zhiyuan Liu and Maosong Sun},
      year={2023},
      eprint={2310.01377},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 52.15
ARC (25-shot) 62.03
HellaSwag (10-shot) 84.36
MMLU (5-shot) 61.07
TruthfulQA (0-shot) 57.45
Winogrande (5-shot) 77.74
GSM8K (5-shot) 12.74
DROP (3-shot) 9.66