Sonya-7B / README.md
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license: cc-by-4.0
language:
  - en
tags:
  - merge

Top 1 Performer MT-bench 🤪

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, and uses a very similar merge. It's a merge of xDAN-AI/xDAN-L1-Chat-RL-v1, Jan-Ai's Stealth v1.2, chargoddard/piano-medley-7b, NeverSleep/Noromaid-7B-v0.2, and 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

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.