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51 lines
3.4 KiB
Python
51 lines
3.4 KiB
Python
from mmengine.config import read_base
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with read_base():
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from opencompass.configs.dataset_collections.chat_OC15 import datasets
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from opencompass.configs.models.hf_llama.hf_llama3_8b_instruct import \
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models as hf_llama3_8b_instruct_model
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from opencompass.configs.summarizers.chat_OC15 import summarizer
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work_dir = 'outputs/debug/llama3-instruct'
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models = sum([v for k, v in locals().items() if k.endswith('_model')], [])
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# dataset version metric mode llama-3-8b-instruct-hf
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# -------------------- --------- ---------------------------- ------ ------------------------
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# average - naive_average gen 55.64
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# mmlu - naive_average gen 68.30
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# cmmlu - naive_average gen 53.29
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# ceval - naive_average gen 52.32
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# GaokaoBench - weighted_average gen 45.91
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# triviaqa_wiki_1shot eaf81e score gen 79.01
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# nq_open_1shot 01cf41 score gen 30.25
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# race-high 9a54b6 accuracy gen 81.22
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# winogrande b36770 accuracy gen 66.46
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# hellaswag e42710 accuracy gen 74.33
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# bbh - naive_average gen 67.25
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# gsm8k 1d7fe4 accuracy gen 79.08
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# math 393424 accuracy gen 27.78
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# TheoremQA 6f0af8 score gen 19.50
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# openai_humaneval 8e312c humaneval_pass@1 gen 55.49
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# sanitized_mbpp 830460 score gen 66.54
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# GPQA_diamond 4baadb accuracy gen 25.76
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# IFEval 3321a3 Prompt-level-strict-accuracy gen 67.84
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# - - - -
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# mmlu - naive_average gen 68.30
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# mmlu-stem - naive_average gen 57.92
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# mmlu-social-science - naive_average gen 77.83
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# mmlu-humanities - naive_average gen 71.20
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# mmlu-other - naive_average gen 71.79
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# cmmlu - naive_average gen 53.29
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# cmmlu-stem - naive_average gen 45.40
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# cmmlu-social-science - naive_average gen 54.63
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# cmmlu-humanities - naive_average gen 54.14
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# cmmlu-other - naive_average gen 59.52
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# cmmlu-china-specific - naive_average gen 49.33
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# ceval - naive_average gen 52.32
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# ceval-stem - naive_average gen 48.16
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# ceval-social-science - naive_average gen 57.50
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# ceval-humanities - naive_average gen 53.26
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# ceval-other - naive_average gen 54.26
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# ceval-hard - naive_average gen 35.59
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