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Adding Evaluation Results
Browse filesThis 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
README.md
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---
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-
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- Qwen/Qwen2.5-14B
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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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# Arcee-SuperNova-Medius
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## Summary
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Arcee-SuperNova-Medius provides a unique balance of power, efficiency, and versatility. By distilling knowledge from two top-performing teacher models into a single 14B parameter model, SuperNova-Medius achieves results that rival larger models while maintaining a compact size ideal for practical deployment. Whether for customer support, content creation, or technical assistance, SuperNova-Medius is the perfect choice for organizations looking to leverage advanced language model capabilities in a cost-effective and accessible form.
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---
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license: apache-2.0
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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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- Qwen/Qwen2.5-14B
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model-index:
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- name: SuperNova-Medius
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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: 55.6
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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/SuperNova-Medius
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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: 49.3
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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/SuperNova-Medius
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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: 32.48
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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/SuperNova-Medius
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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: 17.9
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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/SuperNova-Medius
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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: 19.19
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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/SuperNova-Medius
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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: 48.83
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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/SuperNova-Medius
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name: Open LLM Leaderboard
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---
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# Arcee-SuperNova-Medius
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## Summary
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Arcee-SuperNova-Medius provides a unique balance of power, efficiency, and versatility. By distilling knowledge from two top-performing teacher models into a single 14B parameter model, SuperNova-Medius achieves results that rival larger models while maintaining a compact size ideal for practical deployment. Whether for customer support, content creation, or technical assistance, SuperNova-Medius is the perfect choice for organizations looking to leverage advanced language model capabilities in a cost-effective and accessible form.
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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__SuperNova-Medius)
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| Metric |Value|
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|-------------------|----:|
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|Avg. |37.22|
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|IFEval (0-Shot) |55.60|
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|BBH (3-Shot) |49.30|
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|MATH Lvl 5 (4-Shot)|32.48|
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|GPQA (0-shot) |17.90|
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|MuSR (0-shot) |19.19|
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|MMLU-PRO (5-shot) |48.83|
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