130 lines
5.2 KiB
Markdown
130 lines
5.2 KiB
Markdown
---
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license: cc-by-4.0
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language:
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- en
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tags:
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- merge
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---
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<div style="display: flex; justify-content: center; align-items: center">
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<img src="https://huggingface.co/SanjiWatsuki/Sonya-7B/resolve/main/assets/Sonya.jpg">
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</div
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>
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<p align="center">
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<big><b>Top 1 Performer MT-bench 🤪</b></big>
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</p>
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## WTF is This?
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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.
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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.
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I picked these models because:
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* MT-Bench normally correlates well with real world model quality and xDAN performs well on it.
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* Almost all models in the mix were Alpaca prompt formatted which gives prompt consistency.
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* Stealth v1.2 has been a magic sprinkle that seems to increase my MT-Bench scores.
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* I added RP models because it boosted the Writing and Roleplay benchmarks 👀
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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.
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**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 😉
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**MT-Bench Average Turn**
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| model | score | size
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|--------------------|-----------|--------
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| gpt-4 | 8.99 | -
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| **Sonya-7B** | **8.52** | **7b**
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| xDAN-L1-Chat-RL-v1 | 8.34 | 7b
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| Starling-7B | 8.09 | 7b
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| Claude-2 | 8.06 | -
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| *Silicon-Maid* | *7.96* | *7b*
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| *Loyal-Macaroni-Maid*| *7.95* | *7b*
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| gpt-3.5-turbo | 7.94 | 20b?
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| Claude-1 | 7.90 | -
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| OpenChat-3.5 | 7.81 | -
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| vicuna-33b-v1.3 | 7.12 | 33b
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| wizardlm-30b | 7.01 | 30b
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| Llama-2-70b-chat | 6.86 | 70b
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<img src="https://huggingface.co/SanjiWatsuki/Sonya-7B/resolve/main/assets/mt-bench-gpt.png">
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<img src="https://huggingface.co/SanjiWatsuki/Sonya-7B/resolve/main/assets/mt-bench-comparison.png">
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### The Sauce
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```
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models:
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- model: xDAN-AI/xDAN-L1-Chat-RL-v1
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parameters:
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weight: 1
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density: 1
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- model: chargoddard/piano-medley-7b
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parameters:
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weight: 0.3
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- model: jan-hq/stealth-v1.2
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parameters:
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weight: 0.2
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- model: NeverSleep/Noromaid-7b-v0.2
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parameters:
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weight: 0.2
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- model: athirdpath/NSFW_DPO_vmgb-7b
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parameters:
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weight: 0.2
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merge_method: ties
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base_model: mistralai/Mistral-7B-v0.1
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parameters:
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density: 0.4
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int8_mask: true
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normalize: true
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dtype: bfloat16
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```
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**There was no additional training, finetuning, or DPO.** This is a straight merger.
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### Prompt Template (Alpaca)
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```
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Below is an instruction that describes a task. Write a response that appropriately completes the request.
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### Instruction:
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{prompt}
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### Response:
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```
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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.
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### Other Benchmark Stuff
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**########## First turn ##########**
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| model | turn | score | size
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|--------------------|------|----------|--------
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| **Sonya-7B** | 1 | **9.06875** | **7b**
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| gpt-4 | 1 | 8.95625 | -
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| xDAN-L1-Chat-RL-v1 | 1 | *8.87500* | *7b*
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| xDAN-L2-Chat-RL-v2 | 1 | 8.78750 | 30b
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| claude-v1 | 1 | 8.15000 | -
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| gpt-3.5-turbo | 1 | 8.07500 | 20b
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| vicuna-33b-v1.3 | 1 | 7.45625 | 33b
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| wizardlm-30b | 1 | 7.13125 | 30b
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| oasst-sft-7-llama-30b | 1 | 7.10625 | 30b
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| Llama-2-70b-chat | 1 | 6.98750 | 70b
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########## Second turn ##########
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| model | turn | score | size
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|--------------------|------|-----------|--------
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| gpt-4 | 2 | 9.025000 | -
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| xDAN-L2-Chat-RL-v2 | 2 | 8.087500 | 30b
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| **Sonya-7B** | 2 | **7.962500** | **7b**
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| xDAN-L1-Chat-RL-v1 | 2 | 7.825000 | 7b
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| gpt-3.5-turbo | 2 | 7.812500 | 20b
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| claude-v1 | 2 | 7.650000 | -
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| wizardlm-30b | 2 | 6.887500 | 30b
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| vicuna-33b-v1.3 | 2 | 6.787500 | 33b
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| Llama-2-70b-chat | 2 | 6.725000 | 70b
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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`.
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