---

tags:

• generated_from_trainer 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:

  AI2 Reasoning Challenge (25-Shot)

  • 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

  HellaSwag (10-shot)

  • 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

  DROP (3-shot)

  • 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: 9.662437080536909 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta

  TruthfulQA (0-shot)

  • 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

  GSM8k (5-shot)

  • 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

  MMLU (5-Shot)

  • 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

  Winogrande (5-shot)

  • 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: 77.74269928966061 source: name: Open LLM Leaderboard url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=HuggingFaceH4/zephyr-7b-beta

  AlpacaEval (taken from model card)

  • task: type: text-generation name: Text Generation dataset: name: AlpacaEval type: tatsu-lab/alpaca_eval metrics:
    • type: unknown name: win rate value: 0.9060 source: url: https://tatsu-lab.github.io/alpaca_eval/

  MT-Bench (taken from model card)

  • 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

---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> <img src="https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha/resolve/main/thumbnail.png" alt="Zephyr Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>

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

<!-- Provide the basic links for the model. -->

• 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:

# 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

<!-- This section is meant to convey both technical and sociotechnical 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