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Adding Evaluation Results

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This 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

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  1. README.md +117 -1
README.md CHANGED
@@ -3,6 +3,109 @@ license: llama2
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  tags:
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  - merge
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  - mergekit
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  Llama 2 13b is a pretty decent language model. You know what's probably better? *Two* Llama 2 13b models. In a trenchcoat.
@@ -33,4 +136,17 @@ Benchmark results:
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  This tells us two very important things:
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  1. TruthfulQA is a perfect benchmark in every way.
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- 2. Llama models are amazingly robust to being fed their own output.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  tags:
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  - merge
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  - mergekit
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+ model-index:
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+ - name: llama-2-26b-trenchcoat-stack
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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: 55.03
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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=chargoddard/llama-2-26b-trenchcoat-stack
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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: 79.9
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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=chargoddard/llama-2-26b-trenchcoat-stack
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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: 53.73
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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=chargoddard/llama-2-26b-trenchcoat-stack
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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: 40.48
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+ source:
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+ url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=chargoddard/llama-2-26b-trenchcoat-stack
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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: 74.74
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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=chargoddard/llama-2-26b-trenchcoat-stack
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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: 2.88
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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=chargoddard/llama-2-26b-trenchcoat-stack
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+ name: Open LLM Leaderboard
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  ---
110
 
111
  Llama 2 13b is a pretty decent language model. You know what's probably better? *Two* Llama 2 13b models. In a trenchcoat.
 
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  This tells us two very important things:
138
  1. TruthfulQA is a perfect benchmark in every way.
139
+ 2. Llama models are amazingly robust to being fed their own output.
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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_chargoddard__llama-2-26b-trenchcoat-stack)
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+
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+ | Metric |Value|
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+ |---------------------------------|----:|
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+ |Avg. |51.13|
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+ |AI2 Reasoning Challenge (25-Shot)|55.03|
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+ |HellaSwag (10-Shot) |79.90|
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+ |MMLU (5-Shot) |53.73|
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+ |TruthfulQA (0-shot) |40.48|
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+ |Winogrande (5-shot) |74.74|
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+ |GSM8k (5-shot) | 2.88|
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+