prithivMLmods
commited on
Adding Evaluation Results (#2)
Browse files- Adding Evaluation Results (4fed05fae446c94790c20547dc90cef514e7b09b)
README.md
CHANGED
@@ -10,6 +10,105 @@ tags:
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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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@@ -121,3 +220,18 @@ QwQ-LCoT-14B-Conversational is ideal for:
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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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