---
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**. 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`.