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: cc-by-nc-4.0
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language:
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- ko
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---
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---
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@@ -25,4 +128,17 @@ language:
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### Hardware
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* Hardware: Utilized two A100 (80G*2EA) GPUs for training.
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* Training Factors: This model was fine-tuned with SFT, using the HuggingFace SFTtrainer and applied fsdp. Key training adjustments include the addition of new Korean tokens trained with the SentencePieceBPETokenizer, trained for 2 epochs, batch size of 1, and gradient accumulation of 32.
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* 이 모델은 SFT를 사용하여 HuggingFace SFTtrainer와 fsdp를 적용하여 미세조정되었습니다. 주요 훈련 조정으로는 SentencePieceBPETokenizer로 훈련된 새로운 한글 토큰들을 추가, 2 에폭 훈련, 배치 크기 1, 그리고 그라디언트 누적 32를 포함합니다.
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---
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language:
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- ko
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license: cc-by-nc-4.0
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model-index:
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- name: K2S3-SOLAR-11b-v1.0
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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: 33.7
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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=Changgil/K2S3-SOLAR-11b-v1.0
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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: 51.39
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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=Changgil/K2S3-SOLAR-11b-v1.0
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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: 30.05
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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=Changgil/K2S3-SOLAR-11b-v1.0
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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: 45.99
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Changgil/K2S3-SOLAR-11b-v1.0
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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: 57.54
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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=Changgil/K2S3-SOLAR-11b-v1.0
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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: 1.36
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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=Changgil/K2S3-SOLAR-11b-v1.0
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name: Open LLM Leaderboard
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---
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---
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### Hardware
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* Hardware: Utilized two A100 (80G*2EA) GPUs for training.
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* Training Factors: This model was fine-tuned with SFT, using the HuggingFace SFTtrainer and applied fsdp. Key training adjustments include the addition of new Korean tokens trained with the SentencePieceBPETokenizer, trained for 2 epochs, batch size of 1, and gradient accumulation of 32.
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+
* 이 모델은 SFT를 사용하여 HuggingFace SFTtrainer와 fsdp를 적용하여 미세조정되었습니다. 주요 훈련 조정으로는 SentencePieceBPETokenizer로 훈련된 새로운 한글 토큰들을 추가, 2 에폭 훈련, 배치 크기 1, 그리고 그라디언트 누적 32를 포함합니다.
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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_Changgil__K2S3-SOLAR-11b-v1.0)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |36.67|
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|AI2 Reasoning Challenge (25-Shot)|33.70|
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|HellaSwag (10-Shot) |51.39|
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|MMLU (5-Shot) |30.05|
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|TruthfulQA (0-shot) |45.99|
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|Winogrande (5-shot) |57.54|
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|GSM8k (5-shot) | 1.36|
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