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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.

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  1. README.md +121 -5
README.md CHANGED
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  ---
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  license: other
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- license_name: yi-license
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- license_link: LICENSE
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- datasets:
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- - adamo1139/AEZAKMI_v2
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  tags:
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  - llm
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  - fine-tune
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  - yi
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  ## Model description
@@ -115,4 +218,17 @@ There is also some issue with handling long system messages for RP, I was planni
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  I will probably be working on de-contaminating base Yi-34B model now. \
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  My second run of AEZAKMI v2 fine-tune was just 0.15 epochs and I really like how natural this model is and how rich is it's vocabulary. I will try to train less to hit the sweetspot. \
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  I will be uploading LoRA adapter for that second run that was just 0.15 epochs. \
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- I believe that I might have gotten what I want if I would have stopped training sooner. I don't have checkpoints older than 1500 steps back so I would need to re-run training to get it back.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: other
 
 
 
 
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  tags:
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  - llm
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  - fine-tune
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  - yi
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+ datasets:
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+ - adamo1139/AEZAKMI_v2
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+ license_name: yi-license
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+ license_link: LICENSE
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+ model-index:
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+ - name: Yi-34B-200K-AEZAKMI-v2
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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: 67.92
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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=adamo1139/Yi-34B-200K-AEZAKMI-v2
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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: 85.61
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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=adamo1139/Yi-34B-200K-AEZAKMI-v2
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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: 75.22
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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=adamo1139/Yi-34B-200K-AEZAKMI-v2
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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: 56.74
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+ source:
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+ url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=adamo1139/Yi-34B-200K-AEZAKMI-v2
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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: 81.61
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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=adamo1139/Yi-34B-200K-AEZAKMI-v2
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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: 58.91
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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=adamo1139/Yi-34B-200K-AEZAKMI-v2
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+ name: Open LLM Leaderboard
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  ---
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  ## Model description
 
218
  I will probably be working on de-contaminating base Yi-34B model now. \
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  My second run of AEZAKMI v2 fine-tune was just 0.15 epochs and I really like how natural this model is and how rich is it's vocabulary. I will try to train less to hit the sweetspot. \
220
  I will be uploading LoRA adapter for that second run that was just 0.15 epochs. \
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+ I believe that I might have gotten what I want if I would have stopped training sooner. I don't have checkpoints older than 1500 steps back so I would need to re-run training to get it back.
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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_adamo1139__Yi-34B-200K-AEZAKMI-v2)
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+
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+ | Metric |Value|
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+ |---------------------------------|----:|
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+ |Avg. |71.00|
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+ |AI2 Reasoning Challenge (25-Shot)|67.92|
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+ |HellaSwag (10-Shot) |85.61|
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+ |MMLU (5-Shot) |75.22|
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+ |TruthfulQA (0-shot) |56.74|
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+ |Winogrande (5-shot) |81.61|
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+ |GSM8k (5-shot) |58.91|
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+