初始化项目，由ModelHub XC社区提供模型
Model: kwchoi/DPO_mistral_v01_7b_ultra_0130_1k Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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datasets:
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- argilla/ultrafeedback-binarized-preferences-cleaned
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model-index:
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- name: DPO_mistral_v01_7b_ultra_0130_1k
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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: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 57.17
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_v01_7b_ultra_0130_1k
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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: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 79.16
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_v01_7b_ultra_0130_1k
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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 (5-Shot)
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type: cais/mmlu
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config: all
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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: 55.85
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_v01_7b_ultra_0130_1k
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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: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 55.62
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_v01_7b_ultra_0130_1k
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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: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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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: 72.85
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_v01_7b_ultra_0130_1k
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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: GSM8k (5-shot)
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type: gsm8k
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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: 26.31
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_v01_7b_ultra_0130_1k
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name: Open LLM Leaderboard
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---
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Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performanceTesting Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performanceTesting Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performanceTesting Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance Testing Mistral-Instruct model with Orca DPO dataset. Trying to see the effects of DPO for own study. Used Mistral-7B-Instrcut-v0.2 model due to its good performance
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_kwchoi__DPO_mistral_v01_7b_ultra_0130_1k)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |57.83|
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|AI2 Reasoning Challenge (25-Shot)|57.17|
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|HellaSwag (10-Shot) |79.16|
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|MMLU (5-Shot) |55.85|
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|TruthfulQA (0-shot) |55.62|
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|Winogrande (5-shot) |72.85|
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|GSM8k (5-shot) |26.31|
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