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Adding Evaluation Results (#12)

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- Adding Evaluation Results (1b7fb5b0d8345ee6ae49c32e5f0ab8379d0cacba)


Co-authored-by: Open LLM Leaderboard PR Bot <[email protected]>

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  1. README.md +113 -5
README.md CHANGED
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  ---
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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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  library_name: transformers
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  tags:
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  - mergekit
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  - merge
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- license: other
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  <div style="width: auto; margin-left: auto; margin-right: auto">
@@ -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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+
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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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+