File size: 19,441 Bytes
66c8b2b
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
---
tags:
- generated_from_trainer
model-index:
- name: zephyr-7b-beta
  results: []
license: mit
datasets:
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/ultrafeedback_binarized
language:
- en
base_model: mistralai/Mistral-7B-v0.1
---

<!-- 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](https://huggingface.co/mistralai/Mistral-7B-v0.1) that was trained on on a mix of publicly available, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). We found that removing the in-built alignment of these datasets boosted performance on [MT Bench](https://huggingface.co/spaces/lmsys/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. You can find more details in the [technical report](https://arxiv.org/abs/2310.16944).


## 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](https://huggingface.co/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](https://huggingface.co/spaces/lmsys/mt-bench) and [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) 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](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/raxvt5ma16d7T23my34WC.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`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT. 
We then further aligned the model with [🤗 TRL's](https://github.com/huggingface/trl) `DPOTrainer` on the [openbmb/UltraFeedback](https://huggingface.co/datasets/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](https://huggingface.co/spaces/HuggingFaceH4/zephyr-chat) to test its capabilities. 

You can find the datasets used for training Zephyr-7B-β [here](https://huggingface.co/collections/HuggingFaceH4/zephyr-7b-6538c6d6d5ddd1cbb1744a66)

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

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

Zephyr-7B-β 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](https://huggingface.co/tiiuae/falcon-180B#training-data) 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}
}
```