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 and should only be used for educational and research purposes.

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

Repository: https://github.com/huggingface/alignment-handbook
Demo: https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat

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

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