Adding Evaluation Results (#3)
Browse files- Adding Evaluation Results (2b7b57fa8f674c5c6d9b1afe7d917bd6dbbd86bc)
README.md
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- text-generation-inference
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- phi-4
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- qwq
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---
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![zsdfvdsfvasdfvsdrf.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/vchJIg-Y05Pb7AmCDgCkm.png)
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@@ -112,4 +211,18 @@ While phi-4 o1 ft is a powerful and versatile model, it has certain limitations
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- The model should not be used for malicious purposes, such as generating harmful content, misinformation, or spam. Users are responsible for ensuring ethical use.
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7. **Domain-Specific Limitations**:
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-
- While the model performs well on general-purpose tasks, it may lack depth in highly specialized domains (e.g., medical, legal, or financial fields) without additional fine-tuning.
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- text-generation-inference
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- phi-4
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- qwq
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model-index:
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- name: Phi-4-o1
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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: 2.9
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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%2FPhi-4-o1
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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: 52.17
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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%2FPhi-4-o1
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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: 39.43
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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%2FPhi-4-o1
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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: 17.67
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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%2FPhi-4-o1
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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: 22.15
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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#/?search=prithivMLmods%2FPhi-4-o1
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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: 46.37
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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%2FPhi-4-o1
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name: Open LLM Leaderboard
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---
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![zsdfvdsfvasdfvsdrf.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/vchJIg-Y05Pb7AmCDgCkm.png)
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- The model should not be used for malicious purposes, such as generating harmful content, misinformation, or spam. Users are responsible for ensuring ethical use.
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7. **Domain-Specific Limitations**:
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- While the model performs well on general-purpose tasks, it may lack depth in highly specialized domains (e.g., medical, legal, or financial fields) without additional fine-tuning.
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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__Phi-4-o1-details)!
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Summarized results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/contents/viewer/default/train?q=prithivMLmods%2FPhi-4-o1&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** | 30.11|
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|IFEval (0-Shot) | 2.90|
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|BBH (3-Shot) | 52.17|
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|MATH Lvl 5 (4-Shot)| 39.43|
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|GPQA (0-shot) | 17.67|
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|MuSR (0-shot) | 22.15|
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|MMLU-PRO (5-shot) | 46.37|
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