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Adding Evaluation Results (#12)
Browse files- Adding Evaluation Results (1b7fb5b0d8345ee6ae49c32e5f0ab8379d0cacba)
Co-authored-by: Open LLM Leaderboard PR Bot <[email protected]>
README.md
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---
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-
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- sophosympatheia/Midnight-Miqu-70B-v1.0
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- migtissera/Tess-70B-v1.6
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library_name: transformers
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tags:
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- mergekit
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- merge
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-
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---
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<div style="width: auto; margin-left: auto; margin-right: auto">
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@@ -219,4 +314,17 @@ dtype: float16
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### Notes
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I tried several methods of merging Midnight Miqu v1.0 with Tess v1.6, and this dare_linear approach worked the best by far. I tried the same approach with other Miqu finetunes like ShinojiResearch/Senku-70B-Full and abideen/Liberated-Miqu-70B, but there was a huge difference in performance. The merge with Tess was the best one.
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-
I also tried the SLERP approach I used to create Midnight Miqu v1.0, only using Tess instead of 152334H_miqu-1-70b in that config, and that result was nowhere near as good either.
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---
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license: other
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library_name: transformers
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tags:
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- mergekit
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- merge
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base_model:
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- sophosympatheia/Midnight-Miqu-70B-v1.0
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- migtissera/Tess-70B-v1.6
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model-index:
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- name: Midnight-Miqu-70B-v1.5
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: IFEval (0-Shot)
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type: HuggingFaceH4/ifeval
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args:
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num_few_shot: 0
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metrics:
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- type: inst_level_strict_acc and prompt_level_strict_acc
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value: 61.18
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name: strict accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sophosympatheia/Midnight-Miqu-70B-v1.5
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: BBH (3-Shot)
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type: BBH
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args:
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num_few_shot: 3
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metrics:
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- type: acc_norm
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value: 38.54
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sophosympatheia/Midnight-Miqu-70B-v1.5
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MATH Lvl 5 (4-Shot)
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type: hendrycks/competition_math
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args:
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num_few_shot: 4
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metrics:
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- type: exact_match
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value: 2.42
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name: exact match
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sophosympatheia/Midnight-Miqu-70B-v1.5
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GPQA (0-shot)
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type: Idavidrein/gpqa
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 6.15
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sophosympatheia/Midnight-Miqu-70B-v1.5
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MuSR (0-shot)
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type: TAUR-Lab/MuSR
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args:
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num_few_shot: 0
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metrics:
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- type: acc_norm
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value: 11.65
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name: acc_norm
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sophosympatheia/Midnight-Miqu-70B-v1.5
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU-PRO (5-shot)
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type: TIGER-Lab/MMLU-Pro
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 31.39
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name: accuracy
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source:
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url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=sophosympatheia/Midnight-Miqu-70B-v1.5
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name: Open LLM Leaderboard
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---
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<div style="width: auto; margin-left: auto; margin-right: auto">
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### Notes
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I tried several methods of merging Midnight Miqu v1.0 with Tess v1.6, and this dare_linear approach worked the best by far. I tried the same approach with other Miqu finetunes like ShinojiResearch/Senku-70B-Full and abideen/Liberated-Miqu-70B, but there was a huge difference in performance. The merge with Tess was the best one.
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+
I also tried the SLERP approach I used to create Midnight Miqu v1.0, only using Tess instead of 152334H_miqu-1-70b in that config, and that result was nowhere near as good either.
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_sophosympatheia__Midnight-Miqu-70B-v1.5)
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| Metric |Value|
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|-------------------|----:|
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|Avg. |25.22|
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|IFEval (0-Shot) |61.18|
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|BBH (3-Shot) |38.54|
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|MATH Lvl 5 (4-Shot)| 2.42|
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|GPQA (0-shot) | 6.15|
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|MuSR (0-shot) |11.65|
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|MMLU-PRO (5-shot) |31.39|
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