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Adding Evaluation Results

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This is an automated PR created with https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr

The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card.

If you encounter any issues, please report them to https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr/discussions

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  1. README.md +109 -0
README.md CHANGED
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  license: apache-2.0
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  base_model:
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  - Qwen/Qwen2.5-14B
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  <div align="center">
@@ -45,3 +140,17 @@ Virtuoso-Small is the debut public release of the Virtuoso series of models by A
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  Virtuoso-Small is available under the Apache-2.0 license and can be deployed locally or accessed through an API at [models.arcee.ai](https://models.arcee.ai). For larger-scale or more demanding applications, consider Virtuoso-Forte or Virtuoso-Prime.
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  license: apache-2.0
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  base_model:
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  - Qwen/Qwen2.5-14B
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+ model-index:
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+ - name: Virtuoso-Small
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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: 79.35
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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=arcee-ai/Virtuoso-Small
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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: 50.4
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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=arcee-ai/Virtuoso-Small
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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: 34.29
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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=arcee-ai/Virtuoso-Small
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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: 11.52
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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=arcee-ai/Virtuoso-Small
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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: 14.44
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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=arcee-ai/Virtuoso-Small
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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: 46.57
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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=arcee-ai/Virtuoso-Small
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+ name: Open LLM Leaderboard
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  ---
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  <div align="center">
 
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  Virtuoso-Small is available under the Apache-2.0 license and can be deployed locally or accessed through an API at [models.arcee.ai](https://models.arcee.ai). For larger-scale or more demanding applications, consider Virtuoso-Forte or Virtuoso-Prime.
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+
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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_arcee-ai__Virtuoso-Small)
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+
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+ | Metric |Value|
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+ |-------------------|----:|
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+ |Avg. |39.43|
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+ |IFEval (0-Shot) |79.35|
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+ |BBH (3-Shot) |50.40|
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+ |MATH Lvl 5 (4-Shot)|34.29|
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+ |GPQA (0-shot) |11.52|
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+ |MuSR (0-shot) |14.44|
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+ |MMLU-PRO (5-shot) |46.57|
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+