leaderboard-pr-bot
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Adding Evaluation Results
Browse filesThis is an automated PR created with https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr
The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card.
If you encounter any issues, please report them to https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr/discussions
README.md
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---
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license: other
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datasets:
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- ehartford/wizard_vicuna_70k_unfiltered
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language:
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- en
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tags:
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- uncensored
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---
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This is [wizard-vicuna-13b](https://huggingface.co/junelee/wizard-vicuna-13b) trained against LLaMA-7B with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA.
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@@ -34,3 +137,17 @@ Detailed results can be found [here](https://huggingface.co/datasets/open-llm-le
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| Winogrande (5-shot) | 72.22 |
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| GSM8K (5-shot) | 4.55 |
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| DROP (3-shot) | 23.8 |
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---
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language:
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- en
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license: other
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tags:
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- uncensored
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datasets:
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- ehartford/wizard_vicuna_70k_unfiltered
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model-index:
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- name: Wizard-Vicuna-7B-Uncensored
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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: 53.41
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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=ehartford/Wizard-Vicuna-7B-Uncensored
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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: 78.85
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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=ehartford/Wizard-Vicuna-7B-Uncensored
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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: 37.09
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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=ehartford/Wizard-Vicuna-7B-Uncensored
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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: 43.48
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ehartford/Wizard-Vicuna-7B-Uncensored
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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: 72.22
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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=ehartford/Wizard-Vicuna-7B-Uncensored
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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: 4.55
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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=ehartford/Wizard-Vicuna-7B-Uncensored
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name: Open LLM Leaderboard
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---
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This is [wizard-vicuna-13b](https://huggingface.co/junelee/wizard-vicuna-13b) trained against LLaMA-7B with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA.
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| Winogrande (5-shot) | 72.22 |
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| GSM8K (5-shot) | 4.55 |
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| DROP (3-shot) | 23.8 |
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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_ehartford__Wizard-Vicuna-7B-Uncensored)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |48.27|
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|AI2 Reasoning Challenge (25-Shot)|53.41|
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|HellaSwag (10-Shot) |78.85|
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|MMLU (5-Shot) |37.09|
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|TruthfulQA (0-shot) |43.48|
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|Winogrande (5-shot) |72.22|
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|GSM8k (5-shot) | 4.55|
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