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

Repository: https://github.com/huggingface/alignment-handbook
Demo: https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat
Chatbot Arena: Evaluate Zephyr 7B against 10+ LLMs in the LMSYS arena: http://arena.lmsys.org

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:

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:

python

# 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}
}

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

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	54.45
AI2 Reasoning Challenge (25-Shot)	58.28
HellaSwag (10-Shot)	81.00
MMLU (5-Shot)	53.57
TruthfulQA (0-shot)	46.10
Winogrande (5-shot)	74.74
GSM8k (5-shot)	13.04

CallComply/zephyr-7b-beta-128k

Model Card for Zephyr 7B β

Model description

Model Sources

Performance

Intended uses & limitations

Bias, Risks, and Limitations

Training and evaluation data

Training hyperparameters

Training results

Framework versions

Citation

Open LLM Leaderboard Evaluation Results

Open LLM Leaderboard Evaluation Results

CallComply/
zephyr-7b-beta-128k