Adding Evaluation Results (#2)
Browse files- Adding Evaluation Results (4fed05fae446c94790c20547dc90cef514e7b09b)
    	
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            - CoT
         
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            - Convsersational
         
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            - text-generation-inference
         
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            ---
         
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            # **QwQ-LCoT-14B-Conversational**
         
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            6. **Resource Intensive**: As a large-scale model with 14 billion parameters, it requires substantial computational resources for both inference and deployment, limiting its use in resource-constrained environments.
         
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            7. **Instruction Ambiguity**: The model’s performance can degrade when instructions are ambiguous, vague, or conflicting, potentially leading to outputs that do not align with user expectations.
         
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            - CoT
         
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            - Convsersational
         
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            - text-generation-inference
         
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            model-index:
         
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            - name: QwQ-LCoT-14B-Conversational
         
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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: wis-k/instruction-following-eval
         
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                  split: train
         
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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: 40.47
         
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                  name: averaged accuracy
         
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                source:
         
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                  url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/?search=prithivMLmods%2FQwQ-LCoT-14B-Conversational
         
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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: SaylorTwift/bbh
         
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                  split: test
         
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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: 45.63
         
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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#/?search=prithivMLmods%2FQwQ-LCoT-14B-Conversational
         
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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: lighteval/MATH-Hard
         
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                  split: test
         
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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: 31.42
         
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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#/?search=prithivMLmods%2FQwQ-LCoT-14B-Conversational
         
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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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                  split: train
         
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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: 13.31
         
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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#/?search=prithivMLmods%2FQwQ-LCoT-14B-Conversational
         
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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: 20.62
         
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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#/?search=prithivMLmods%2FQwQ-LCoT-14B-Conversational
         
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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: 47.54
         
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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#/?search=prithivMLmods%2FQwQ-LCoT-14B-Conversational
         
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                  name: Open LLM Leaderboard
         
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            ---
         
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            # **QwQ-LCoT-14B-Conversational**
         
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            6. **Resource Intensive**: As a large-scale model with 14 billion parameters, it requires substantial computational resources for both inference and deployment, limiting its use in resource-constrained environments.
         
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            7. **Instruction Ambiguity**: The model’s performance can degrade when instructions are ambiguous, vague, or conflicting, potentially leading to outputs that do not align with user expectations.
         
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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/prithivMLmods__QwQ-LCoT-14B-Conversational-details)!
         
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            Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FQwQ-LCoT-14B-Conversational&sort[column]=Average%20%E2%AC%86%EF%B8%8F&sort[direction]=desc)!
         
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            |      Metric       |Value (%)|
         
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            |-------------------|--------:|
         
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            |**Average**        |    33.16|
         
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            |IFEval (0-Shot)    |    40.47|
         
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            |BBH (3-Shot)       |    45.63|
         
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            |MATH Lvl 5 (4-Shot)|    31.42|
         
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            |GPQA (0-shot)      |    13.31|
         
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            |MuSR (0-shot)      |    20.62|
         
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            |MMLU-PRO (5-shot)  |    47.54|
         
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