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- license: cc-by-nc-4.0
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  language:
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  - ko
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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  ---
109
 
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  ---
 
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  ### Hardware
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  * Hardware: Utilized two A100 (80G*2EA) GPUs for training.
130
  * 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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+
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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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+