Hugging Face's logo

HuggingFaceH4
/
zephyr-7b-alpha

Text Generation
Transformers
PyTorch
Safetensors
English
mistral
Generated from Trainer
conversational
text-generation-inference
Inference Endpoints
Model card Files Community
39
zephyr-7b-alpha / README.md
lewtun's picture
lewtun HF staff
Update README.md
014792b verified
preview code
|
raw
history blame contribute delete
10.9 kB
metadata
tags:
  - generated_from_trainer
model-index:
  - name: zephyr-7b-alpha
    results: []
license: mit
datasets:
  - stingning/ultrachat
  - openbmb/UltraFeedback
language:
  - en
base_model: mistralai/Mistral-7B-v0.1
Zephyr Logo

Model Card for Zephyr 7B Alpha

Zephyr is a series of language models that are trained to act as helpful assistants. Zephyr-7B-α is the first 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.

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

Intended uses & limitations

The model was initially fine-tuned on a variant 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 contain 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.

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-alpha", 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-α has not been aligned to human preferences with techniques like RLHF 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

Zephyr 7B Alpha achieves the following results on the evaluation set:

  • Loss: 0.4605
  • Rewards/chosen: -0.5053
  • Rewards/rejected: -1.8752
  • Rewards/accuracies: 0.7812
  • Rewards/margins: 1.3699
  • Logps/rejected: -327.4286
  • Logps/chosen: -297.1040
  • Logits/rejected: -2.7153
  • Logits/chosen: -2.7447

Training procedure

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: 1

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen
0.5602 0.05 100 0.5589 -0.3359 -0.8168 0.7188 0.4809 -306.2607 -293.7161 -2.6554 -2.6797
0.4852 0.1 200 0.5136 -0.5310 -1.4994 0.8125 0.9684 -319.9124 -297.6181 -2.5762 -2.5957
0.5212 0.15 300 0.5168 -0.1686 -1.1760 0.7812 1.0074 -313.4444 -290.3699 -2.6865 -2.7125
0.5496 0.21 400 0.4835 -0.1617 -1.7170 0.8281 1.5552 -324.2635 -290.2326 -2.7947 -2.8218
0.5209 0.26 500 0.5054 -0.4778 -1.6604 0.7344 1.1826 -323.1325 -296.5546 -2.8388 -2.8667
0.4617 0.31 600 0.4910 -0.3738 -1.5180 0.7656 1.1442 -320.2848 -294.4741 -2.8234 -2.8521
0.4452 0.36 700 0.4838 -0.4591 -1.6576 0.7031 1.1986 -323.0770 -296.1796 -2.7401 -2.7653
0.4674 0.41 800 0.5077 -0.5692 -1.8659 0.7656 1.2967 -327.2416 -298.3818 -2.6740 -2.6945
0.4656 0.46 900 0.4927 -0.5279 -1.6614 0.7656 1.1335 -323.1518 -297.5553 -2.7817 -2.8015
0.4102 0.52 1000 0.4772 -0.5767 -2.0667 0.7656 1.4900 -331.2578 -298.5311 -2.7160 -2.7455
0.4663 0.57 1100 0.4740 -0.8038 -2.1018 0.7656 1.2980 -331.9604 -303.0741 -2.6994 -2.7257
0.4737 0.62 1200 0.4716 -0.3783 -1.7015 0.7969 1.3232 -323.9545 -294.5634 -2.6842 -2.7135
0.4259 0.67 1300 0.4866 -0.6239 -1.9703 0.7812 1.3464 -329.3312 -299.4761 -2.7046 -2.7356
0.4935 0.72 1400 0.4747 -0.5626 -1.7600 0.7812 1.1974 -325.1243 -298.2491 -2.7153 -2.7444
0.4211 0.77 1500 0.4645 -0.6099 -1.9993 0.7656 1.3894 -329.9109 -299.1959 -2.6944 -2.7236
0.4931 0.83 1600 0.4684 -0.6798 -2.1082 0.7656 1.4285 -332.0890 -300.5934 -2.7006 -2.7305
0.5029 0.88 1700 0.4595 -0.5063 -1.8951 0.7812 1.3889 -327.8267 -297.1233 -2.7108 -2.7403
0.4965 0.93 1800 0.4613 -0.5561 -1.9079 0.7812 1.3518 -328.0831 -298.1203 -2.7226 -2.7523
0.4337 0.98 1900 0.4608 -0.5066 -1.8718 0.7656 1.3652 -327.3599 -297.1296 -2.7175 -2.7469

Framework versions

  • Transformers 4.34.0
  • Pytorch 2.0.1+cu118
  • Datasets 2.12.0
  • Tokenizers 0.14.0

Citation

If you find Zephyr-7B-α 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}
}