README.md

---
language:
- en
license: apache-2.0
datasets:
- argilla/ultrafeedback-binarized-preferences-cleaned
model-index:
- name: DPO_mistral_v01_7b_ultra_0131_1k_1epoch
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 55.97
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_v01_7b_ultra_0131_1k_1epoch
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 76.78
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_v01_7b_ultra_0131_1k_1epoch
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 55.97
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_v01_7b_ultra_0131_1k_1epoch
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 57.94
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_v01_7b_ultra_0131_1k_1epoch
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 73.4
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_v01_7b_ultra_0131_1k_1epoch
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 29.87
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kwchoi/DPO_mistral_v01_7b_ultra_0131_1k_1epoch
      name: Open LLM Leaderboard
---
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
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_kwchoi__DPO_mistral_v01_7b_ultra_0131_1k_1epoch)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |58.32|
|AI2 Reasoning Challenge (25-Shot)|55.97|
|HellaSwag (10-Shot)              |76.78|
|MMLU (5-Shot)                    |55.97|
|TruthfulQA (0-shot)              |57.94|
|Winogrande (5-shot)              |73.40|
|GSM8k (5-shot)                   |29.87|