Hugging Face's logo

SanjiWatsuki
/
Sonya-7B

Text Generation
Transformers
Safetensors
English
mistral
Merge
text-generation-inference
Inference Endpoints
Model card Files Community
3
Sonya-7B
File size: 5,346 Bytes 
8d0a542
 
8426b32
 
 
 
8d0a542
8426b32
 
 
 
 
 
 
 
 
 
 
 
228e7ab
8426b32
c2cfd89
8426b32
 
 
 
 
 
 
61575c1
8426b32
228e7ab
8426b32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c69bcea
 
8426b32
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
c69bcea
 
 
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
 
---
license: cc-by-4.0
language:
- en
tags:
- merge
---

<div style="display: flex; justify-content: center; align-items: center">
  <img src="https://huggingface.co/SanjiWatsuki/Sonya-7B/resolve/main/assets/Sonya.jpg">
</div
>

<p align="center">
  <big><b>Top 1 Performer MT-bench 🤪</b></big>
</p>

## WTF is This?

Sonya-7B is, at the time of writing, the **#1 performing model in MT-Bench first turn, ahead of GPT-4, and overall the #2 model in MT-Bench**, to the best of my knowledge. Sonya-7B should be a good all-purpose model for all tasks including assistant, RP, etc.

Sonya-7B has a similar structure to my previous model, [Silicon-Maid-7B](https://huggingface.co/SanjiWatsuki/Silicon-Maid-7B), and uses a very similar merge. It's a merge of [xDAN-AI/xDAN-L1-Chat-RL-v1](https://huggingface.co/xDAN-AI/xDAN-L1-Chat-RL-v1), [Jan-Ai's Stealth v1.2](https://huggingface.co/jan-hq/stealth-v1.2), [chargoddard/piano-medley-7b](https://huggingface.co/chargoddard/piano-medley-7b), [NeverSleep/Noromaid-7B-v0.2](https://huggingface.co/NeverSleep/Noromaid-7b-v0.2), and [athirdpath/NSFW_DPO_vmgb-7b](athirdpath/NSFW_DPO_vmgb-7b). Sauce is below. Somehow, by combining these pieces, it substantially outscores any of its parents on MT-Bench.

I picked these models because:
* MT-Bench normally correlates well with real world model quality and xDAN performs well on it.
* Almost all models in the mix were Alpaca prompt formatted which gives prompt consistency.
* Stealth v1.2 has been a magic sprinkle that seems to increase my MT-Bench scores.
* I added RP models because it boosted the Writing and Roleplay benchmarks 👀

Based on the parent models, I expect this model to be used with an 8192 context window. Please use NTK scaling alpha of 2.6 to experimentally try out 16384 context.

**Let me be candid:** Despite the test scores, this model is **NOT is a GPT killer**. I think it's a very sharp model **for a 7B**, it probably punches way above its weight **for a 7B**, but it's still a 7B model. Even for a 7B model, I think **it's quirky and has some weird outputs**, probably due to how Frankenstein this merge is. Keep your expectations in check 😉

**MT-Bench Average Turn**
| model              | score     | size
|--------------------|-----------|--------
| gpt-4              | 8.99      |  -
| **Sonya-7B**         | **8.52**      |  **7b**
| xDAN-L1-Chat-RL-v1 | 8.34      |  7b
| Starling-7B        | 8.09      |  7b
| Claude-2           | 8.06      |  -
| *Silicon-Maid*   | *7.96*  |  *7b*
| *Loyal-Macaroni-Maid*| *7.95*      |  *7b*
| gpt-3.5-turbo      | 7.94      |  20b?
| Claude-1           | 7.90      |  -
| OpenChat-3.5       | 7.81      |  -
| vicuna-33b-v1.3    | 7.12      |  33b
| wizardlm-30b       | 7.01      |  30b
| Llama-2-70b-chat   | 6.86      |  70b

<img src="https://huggingface.co/SanjiWatsuki/Sonya-7B/resolve/main/assets/mt-bench-gpt.png">

<img src="https://huggingface.co/SanjiWatsuki/Sonya-7B/resolve/main/assets/mt-bench-comparison.png">

### The Sauce

```
models:
  - model: xDAN-AI/xDAN-L1-Chat-RL-v1
    parameters:
      weight: 1
      density: 1
  - model: chargoddard/piano-medley-7b
    parameters:
      weight: 0.3
  - model: jan-hq/stealth-v1.2
    parameters:
      weight: 0.2
  - model: NeverSleep/Noromaid-7b-v0.2
    parameters:
      weight: 0.2
  - model: athirdpath/NSFW_DPO_vmgb-7b
    parameters:
      weight: 0.2
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  density: 0.4
  int8_mask: true
  normalize: true
dtype: bfloat16
```

**There was no additional training, finetuning, or DPO.** This is a straight merger.

### Prompt Template (Alpaca)

```
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:
```

I found that this model **performed worse** with the xDAN prompt format so, despite the heavy weight of xDAN in this merger, I recommeend *against* its use.

### Other Benchmark Stuff

**########## First turn ##########**
| model              | turn | score    | size
|--------------------|------|----------|--------
| **Sonya-7B** | 1    | **9.06875**  |  **7b**
| gpt-4              | 1    | 8.95625  |  -
| xDAN-L1-Chat-RL-v1 | 1    | *8.87500*  |  *7b*
| xDAN-L2-Chat-RL-v2 | 1    | 8.78750  |  30b
| claude-v1          | 1    | 8.15000  |  -
| gpt-3.5-turbo      | 1    | 8.07500  |  20b
| vicuna-33b-v1.3    | 1    | 7.45625  |  33b
| wizardlm-30b       | 1    | 7.13125  |  30b
| oasst-sft-7-llama-30b | 1 | 7.10625  |  30b
| Llama-2-70b-chat   | 1    | 6.98750  |  70b


########## Second turn ##########
| model              | turn | score     | size
|--------------------|------|-----------|--------
| gpt-4              | 2    | 9.025000  |  -
| xDAN-L2-Chat-RL-v2 | 2    | 8.087500  |  30b
| **Sonya-7B**       | 2    | **7.962500**  |  **7b**
| xDAN-L1-Chat-RL-v1 | 2   | 7.825000  |   7b
| gpt-3.5-turbo      | 2    | 7.812500  |  20b
| claude-v1          | 2    | 7.650000  |  -
| wizardlm-30b       | 2    | 6.887500  |  30b
| vicuna-33b-v1.3    | 2    | 6.787500  |  33b
| Llama-2-70b-chat   | 2    | 6.725000  |  70b

If you'd like to replicate the MT-Bench run, please ensure that the Alpaca prompt template is applied to the model. I did this by putting "alpaca" in the model path to trigger the `AlpacaAdapter`.