{ "results": { "mmlu_pro": { "exact_match,custom-extract": 0.6050531914893617, "exact_match_stderr,custom-extract": 0.004324280084491081, "alias": "mmlu_pro" }, "mmlu_pro_biology": { "alias": " - biology", "exact_match,custom-extract": 0.797768479776848, "exact_match_stderr,custom-extract": 0.01501088675930961 }, "mmlu_pro_business": { "alias": " - business", "exact_match,custom-extract": 0.6501901140684411, "exact_match_stderr,custom-extract": 0.01698920714561709 }, "mmlu_pro_chemistry": { "alias": " - chemistry", "exact_match,custom-extract": 0.4628975265017668, "exact_match_stderr,custom-extract": 0.014826536252330106 }, "mmlu_pro_computer_science": { "alias": " - computer_science", "exact_match,custom-extract": 0.6292682926829268, "exact_match_stderr,custom-extract": 0.023882849188210376 }, "mmlu_pro_economics": { "alias": " - economics", "exact_match,custom-extract": 0.7571090047393365, "exact_match_stderr,custom-extract": 0.01476968134954848 }, "mmlu_pro_engineering": { "alias": " - engineering", "exact_match,custom-extract": 0.4107327141382869, "exact_match_stderr,custom-extract": 0.015812412469129674 }, "mmlu_pro_health": { "alias": " - health", "exact_match,custom-extract": 0.6894865525672371, "exact_match_stderr,custom-extract": 0.01618795835147117 }, "mmlu_pro_history": { "alias": " - history", "exact_match,custom-extract": 0.6456692913385826, "exact_match_stderr,custom-extract": 0.02453678535763431 }, "mmlu_pro_law": { "alias": " - law", "exact_match,custom-extract": 0.46684831970935514, "exact_match_stderr,custom-extract": 0.01504239361072275 }, "mmlu_pro_math": { "alias": " - math", "exact_match,custom-extract": 0.5758697261287935, "exact_match_stderr,custom-extract": 0.013450699683222997 }, "mmlu_pro_other": { "alias": " - other", "exact_match,custom-extract": 0.6829004329004329, "exact_match_stderr,custom-extract": 0.015317068975451516 }, "mmlu_pro_philosophy": { "alias": " - philosophy", "exact_match,custom-extract": 0.6132264529058116, "exact_match_stderr,custom-extract": 0.02182348732721747 }, "mmlu_pro_physics": { "alias": " - physics", "exact_match,custom-extract": 0.5481139337952271, "exact_match_stderr,custom-extract": 0.013813780478397373 }, "mmlu_pro_psychology": { "alias": " - psychology", "exact_match,custom-extract": 0.7832080200501254, "exact_match_stderr,custom-extract": 0.014595904333460285 } }, "groups": { "mmlu_pro": { "exact_match,custom-extract": 0.6050531914893617, "exact_match_stderr,custom-extract": 0.004324280084491081, "alias": "mmlu_pro" } }, "group_subtasks": { "mmlu_pro": [ "mmlu_pro_biology", "mmlu_pro_business", "mmlu_pro_chemistry", "mmlu_pro_computer_science", "mmlu_pro_economics", "mmlu_pro_engineering", "mmlu_pro_health", "mmlu_pro_history", "mmlu_pro_law", "mmlu_pro_math", "mmlu_pro_other", "mmlu_pro_philosophy", "mmlu_pro_physics", "mmlu_pro_psychology" ] }, "configs": { "mmlu_pro_biology": { "task": "mmlu_pro_biology", "task_alias": "biology", "dataset_path": "TIGER-Lab/MMLU-Pro", "test_split": "test", "fewshot_split": "validation", "process_docs": "functools.partial(, subject='biology')", "doc_to_text": "functools.partial(, including_answer=False)", "doc_to_target": "answer", "description": "The following are multiple choice questions (with answers) about biology. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n", "doc_to_text": "functools.partial(, including_answer=True)", "doc_to_target": "" }, "num_fewshot": 5, "metric_list": [ { "metric": "exact_match", "aggregation": "mean", "higher_is_better": true, "ignore_case": true, "ignore_punctuation": true } ], "output_type": "generate_until", "generation_kwargs": { "until": [ "", "Q:", "<|im_end|>" ], "do_sample": false, "temperature": 0.0 }, "repeats": 1, "filter_list": [ { "name": "custom-extract", "filter": [ { "function": "regex", "regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?" }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.0 } }, "mmlu_pro_business": { "task": "mmlu_pro_business", "task_alias": "business", "dataset_path": "TIGER-Lab/MMLU-Pro", "test_split": "test", "fewshot_split": "validation", "process_docs": "functools.partial(, subject='business')", "doc_to_text": "functools.partial(, including_answer=False)", "doc_to_target": "answer", "description": "The following are multiple choice questions (with answers) about business. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n", "doc_to_text": "functools.partial(, including_answer=True)", "doc_to_target": "" }, "num_fewshot": 5, "metric_list": [ { "metric": "exact_match", "aggregation": "mean", "higher_is_better": true, "ignore_case": true, "ignore_punctuation": true } ], "output_type": "generate_until", "generation_kwargs": { "until": [ "", "Q:", "<|im_end|>" ], "do_sample": false, "temperature": 0.0 }, "repeats": 1, "filter_list": [ { "name": "custom-extract", "filter": [ { "function": "regex", "regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?" }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.0 } }, "mmlu_pro_chemistry": { "task": "mmlu_pro_chemistry", "task_alias": "chemistry", "dataset_path": "TIGER-Lab/MMLU-Pro", "test_split": "test", "fewshot_split": "validation", "process_docs": "functools.partial(, subject='chemistry')", "doc_to_text": "functools.partial(, including_answer=False)", "doc_to_target": "answer", "description": "The following are multiple choice questions (with answers) about chemistry. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n", "doc_to_text": "functools.partial(, including_answer=True)", "doc_to_target": "" }, "num_fewshot": 5, "metric_list": [ { "metric": "exact_match", "aggregation": "mean", "higher_is_better": true, "ignore_case": true, "ignore_punctuation": true } ], "output_type": "generate_until", "generation_kwargs": { "until": [ "", "Q:", "<|im_end|>" ], "do_sample": false, "temperature": 0.0 }, "repeats": 1, "filter_list": [ { "name": "custom-extract", "filter": [ { "function": "regex", "regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?" }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.0 } }, "mmlu_pro_computer_science": { "task": "mmlu_pro_computer_science", "task_alias": "computer_science", "dataset_path": "TIGER-Lab/MMLU-Pro", "test_split": "test", "fewshot_split": "validation", "process_docs": "functools.partial(, subject='computer science')", "doc_to_text": "functools.partial(, including_answer=False)", "doc_to_target": "answer", "description": "The following are multiple choice questions (with answers) about computer science. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n", "doc_to_text": "functools.partial(, including_answer=True)", "doc_to_target": "" }, "num_fewshot": 5, "metric_list": [ { "metric": "exact_match", "aggregation": "mean", "higher_is_better": true, "ignore_case": true, "ignore_punctuation": true } ], "output_type": "generate_until", "generation_kwargs": { "until": [ "", "Q:", "<|im_end|>" ], "do_sample": false, "temperature": 0.0 }, "repeats": 1, "filter_list": [ { "name": "custom-extract", "filter": [ { "function": "regex", "regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?" }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.0 } }, "mmlu_pro_economics": { "task": "mmlu_pro_economics", "task_alias": "economics", "dataset_path": "TIGER-Lab/MMLU-Pro", "test_split": "test", "fewshot_split": "validation", "process_docs": "functools.partial(, subject='economics')", "doc_to_text": "functools.partial(, including_answer=False)", "doc_to_target": "answer", "description": "The following are multiple choice questions (with answers) about economics. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n", "doc_to_text": "functools.partial(, including_answer=True)", "doc_to_target": "" }, "num_fewshot": 5, "metric_list": [ { "metric": "exact_match", "aggregation": "mean", "higher_is_better": true, "ignore_case": true, "ignore_punctuation": true } ], "output_type": "generate_until", "generation_kwargs": { "until": [ "", "Q:", "<|im_end|>" ], "do_sample": false, "temperature": 0.0 }, "repeats": 1, "filter_list": [ { "name": "custom-extract", "filter": [ { "function": "regex", "regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?" }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.0 } }, "mmlu_pro_engineering": { "task": "mmlu_pro_engineering", "task_alias": "engineering", "dataset_path": "TIGER-Lab/MMLU-Pro", "test_split": "test", "fewshot_split": "validation", "process_docs": "functools.partial(, subject='engineering')", "doc_to_text": "functools.partial(, including_answer=False)", "doc_to_target": "answer", "description": "The following are multiple choice questions (with answers) about engineering. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n", "doc_to_text": "functools.partial(, including_answer=True)", "doc_to_target": "" }, "num_fewshot": 5, "metric_list": [ { "metric": "exact_match", "aggregation": "mean", "higher_is_better": true, "ignore_case": true, "ignore_punctuation": true } ], "output_type": "generate_until", "generation_kwargs": { "until": [ "", "Q:", "<|im_end|>" ], "do_sample": false, "temperature": 0.0 }, "repeats": 1, "filter_list": [ { "name": "custom-extract", "filter": [ { "function": "regex", "regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?" }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.0 } }, "mmlu_pro_health": { "task": "mmlu_pro_health", "task_alias": "health", "dataset_path": "TIGER-Lab/MMLU-Pro", "test_split": "test", "fewshot_split": "validation", "process_docs": "functools.partial(, subject='health')", "doc_to_text": "functools.partial(, including_answer=False)", "doc_to_target": "answer", "description": "The following are multiple choice questions (with answers) about health. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n", "doc_to_text": "functools.partial(, including_answer=True)", "doc_to_target": "" }, "num_fewshot": 5, "metric_list": [ { "metric": "exact_match", "aggregation": "mean", "higher_is_better": true, "ignore_case": true, "ignore_punctuation": true } ], "output_type": "generate_until", "generation_kwargs": { "until": [ "", "Q:", "<|im_end|>" ], "do_sample": false, "temperature": 0.0 }, "repeats": 1, "filter_list": [ { "name": "custom-extract", "filter": [ { "function": "regex", "regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?" }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.0 } }, "mmlu_pro_history": { "task": "mmlu_pro_history", "task_alias": "history", "dataset_path": "TIGER-Lab/MMLU-Pro", "test_split": "test", "fewshot_split": "validation", "process_docs": "functools.partial(, subject='history')", "doc_to_text": "functools.partial(, including_answer=False)", "doc_to_target": "answer", "description": "The following are multiple choice questions (with answers) about history. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n", "doc_to_text": "functools.partial(, including_answer=True)", "doc_to_target": "" }, "num_fewshot": 5, "metric_list": [ { "metric": "exact_match", "aggregation": "mean", "higher_is_better": true, "ignore_case": true, "ignore_punctuation": true } ], "output_type": "generate_until", "generation_kwargs": { "until": [ "", "Q:", "<|im_end|>" ], "do_sample": false, "temperature": 0.0 }, "repeats": 1, "filter_list": [ { "name": "custom-extract", "filter": [ { "function": "regex", "regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?" }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.0 } }, "mmlu_pro_law": { "task": "mmlu_pro_law", "task_alias": "law", "dataset_path": "TIGER-Lab/MMLU-Pro", "test_split": "test", "fewshot_split": "validation", "process_docs": "functools.partial(, subject='law')", "doc_to_text": "functools.partial(, including_answer=False)", "doc_to_target": "answer", "description": "The following are multiple choice questions (with answers) about law. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n", "doc_to_text": "functools.partial(, including_answer=True)", "doc_to_target": "" }, "num_fewshot": 5, "metric_list": [ { "metric": "exact_match", "aggregation": "mean", "higher_is_better": true, "ignore_case": true, "ignore_punctuation": true } ], "output_type": "generate_until", "generation_kwargs": { "until": [ "", "Q:", "<|im_end|>" ], "do_sample": false, "temperature": 0.0 }, "repeats": 1, "filter_list": [ { "name": "custom-extract", "filter": [ { "function": "regex", "regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?" }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.0 } }, "mmlu_pro_math": { "task": "mmlu_pro_math", "task_alias": "math", "dataset_path": "TIGER-Lab/MMLU-Pro", "test_split": "test", "fewshot_split": "validation", "process_docs": "functools.partial(, subject='math')", "doc_to_text": "functools.partial(, including_answer=False)", "doc_to_target": "answer", "description": "The following are multiple choice questions (with answers) about math. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n", "doc_to_text": "functools.partial(, including_answer=True)", "doc_to_target": "" }, "num_fewshot": 5, "metric_list": [ { "metric": "exact_match", "aggregation": "mean", "higher_is_better": true, "ignore_case": true, "ignore_punctuation": true } ], "output_type": "generate_until", "generation_kwargs": { "until": [ "", "Q:", "<|im_end|>" ], "do_sample": false, "temperature": 0.0 }, "repeats": 1, "filter_list": [ { "name": "custom-extract", "filter": [ { "function": "regex", "regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?" }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.0 } }, "mmlu_pro_other": { "task": "mmlu_pro_other", "task_alias": "other", "dataset_path": "TIGER-Lab/MMLU-Pro", "test_split": "test", "fewshot_split": "validation", "process_docs": "functools.partial(, subject='other')", "doc_to_text": "functools.partial(, including_answer=False)", "doc_to_target": "answer", "description": "The following are multiple choice questions (with answers) about other. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n", "doc_to_text": "functools.partial(, including_answer=True)", "doc_to_target": "" }, "num_fewshot": 5, "metric_list": [ { "metric": "exact_match", "aggregation": "mean", "higher_is_better": true, "ignore_case": true, "ignore_punctuation": true } ], "output_type": "generate_until", "generation_kwargs": { "until": [ "", "Q:", "<|im_end|>" ], "do_sample": false, "temperature": 0.0 }, "repeats": 1, "filter_list": [ { "name": "custom-extract", "filter": [ { "function": "regex", "regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?" }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.0 } }, "mmlu_pro_philosophy": { "task": "mmlu_pro_philosophy", "task_alias": "philosophy", "dataset_path": "TIGER-Lab/MMLU-Pro", "test_split": "test", "fewshot_split": "validation", "process_docs": "functools.partial(, subject='philosophy')", "doc_to_text": "functools.partial(, including_answer=False)", "doc_to_target": "answer", "description": "The following are multiple choice questions (with answers) about philosophy. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n", "doc_to_text": "functools.partial(, including_answer=True)", "doc_to_target": "" }, "num_fewshot": 5, "metric_list": [ { "metric": "exact_match", "aggregation": "mean", "higher_is_better": true, "ignore_case": true, "ignore_punctuation": true } ], "output_type": "generate_until", "generation_kwargs": { "until": [ "", "Q:", "<|im_end|>" ], "do_sample": false, "temperature": 0.0 }, "repeats": 1, "filter_list": [ { "name": "custom-extract", "filter": [ { "function": "regex", "regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?" }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.0 } }, "mmlu_pro_physics": { "task": "mmlu_pro_physics", "task_alias": "physics", "dataset_path": "TIGER-Lab/MMLU-Pro", "test_split": "test", "fewshot_split": "validation", "process_docs": "functools.partial(, subject='physics')", "doc_to_text": "functools.partial(, including_answer=False)", "doc_to_target": "answer", "description": "The following are multiple choice questions (with answers) about physics. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n", "doc_to_text": "functools.partial(, including_answer=True)", "doc_to_target": "" }, "num_fewshot": 5, "metric_list": [ { "metric": "exact_match", "aggregation": "mean", "higher_is_better": true, "ignore_case": true, "ignore_punctuation": true } ], "output_type": "generate_until", "generation_kwargs": { "until": [ "", "Q:", "<|im_end|>" ], "do_sample": false, "temperature": 0.0 }, "repeats": 1, "filter_list": [ { "name": "custom-extract", "filter": [ { "function": "regex", "regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?" }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.0 } }, "mmlu_pro_psychology": { "task": "mmlu_pro_psychology", "task_alias": "psychology", "dataset_path": "TIGER-Lab/MMLU-Pro", "test_split": "test", "fewshot_split": "validation", "process_docs": "functools.partial(, subject='psychology')", "doc_to_text": "functools.partial(, including_answer=False)", "doc_to_target": "answer", "description": "The following are multiple choice questions (with answers) about psychology. Think step by step and then finish your answer with \"the answer is (X)\" where X is the correct letter choice.\n", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "fewshot_config": { "sampler": "first_n", "doc_to_text": "functools.partial(, including_answer=True)", "doc_to_target": "" }, "num_fewshot": 5, "metric_list": [ { "metric": "exact_match", "aggregation": "mean", "higher_is_better": true, "ignore_case": true, "ignore_punctuation": true } ], "output_type": "generate_until", "generation_kwargs": { "until": [ "", "Q:", "<|im_end|>" ], "do_sample": false, "temperature": 0.0 }, "repeats": 1, "filter_list": [ { "name": "custom-extract", "filter": [ { "function": "regex", "regex_pattern": "answer is \\(?([ABCDEFGHIJ])\\)?" }, { "function": "take_first" } ] } ], "should_decontaminate": false, "metadata": { "version": 1.0 } } }, "versions": { "mmlu_pro": 2.0, "mmlu_pro_biology": 1.0, "mmlu_pro_business": 1.0, "mmlu_pro_chemistry": 1.0, "mmlu_pro_computer_science": 1.0, "mmlu_pro_economics": 1.0, "mmlu_pro_engineering": 1.0, "mmlu_pro_health": 1.0, "mmlu_pro_history": 1.0, "mmlu_pro_law": 1.0, "mmlu_pro_math": 1.0, "mmlu_pro_other": 1.0, "mmlu_pro_philosophy": 1.0, "mmlu_pro_physics": 1.0, "mmlu_pro_psychology": 1.0 }, "n-shot": { "mmlu_pro_biology": 5, "mmlu_pro_business": 5, "mmlu_pro_chemistry": 5, "mmlu_pro_computer_science": 5, "mmlu_pro_economics": 5, "mmlu_pro_engineering": 5, "mmlu_pro_health": 5, "mmlu_pro_history": 5, "mmlu_pro_law": 5, "mmlu_pro_math": 5, "mmlu_pro_other": 5, "mmlu_pro_philosophy": 5, "mmlu_pro_physics": 5, "mmlu_pro_psychology": 5 }, "higher_is_better": { "mmlu_pro": { "exact_match": true }, "mmlu_pro_biology": { "exact_match": true }, "mmlu_pro_business": { "exact_match": true }, "mmlu_pro_chemistry": { "exact_match": true }, "mmlu_pro_computer_science": { "exact_match": true }, "mmlu_pro_economics": { "exact_match": true }, "mmlu_pro_engineering": { "exact_match": true }, "mmlu_pro_health": { "exact_match": true }, "mmlu_pro_history": { "exact_match": true }, "mmlu_pro_law": { "exact_match": true }, "mmlu_pro_math": { "exact_match": true }, "mmlu_pro_other": { "exact_match": true }, "mmlu_pro_philosophy": { "exact_match": true }, "mmlu_pro_physics": { "exact_match": true }, "mmlu_pro_psychology": { "exact_match": true } }, "n-samples": { "mmlu_pro_biology": { "original": 717, "effective": 717 }, "mmlu_pro_business": { "original": 789, "effective": 789 }, "mmlu_pro_chemistry": { "original": 1132, "effective": 1132 }, "mmlu_pro_computer_science": { "original": 410, "effective": 410 }, "mmlu_pro_economics": { "original": 844, "effective": 844 }, "mmlu_pro_engineering": { "original": 969, "effective": 969 }, "mmlu_pro_health": { "original": 818, "effective": 818 }, "mmlu_pro_history": { "original": 381, "effective": 381 }, "mmlu_pro_law": { "original": 1101, "effective": 1101 }, "mmlu_pro_math": { "original": 1351, "effective": 1351 }, "mmlu_pro_other": { "original": 924, "effective": 924 }, "mmlu_pro_philosophy": { "original": 499, "effective": 499 }, "mmlu_pro_physics": { "original": 1299, "effective": 1299 }, "mmlu_pro_psychology": { "original": 798, "effective": 798 } }, "config": { "model": "vllm", "model_args": "pretrained=meta-llama/Llama-3.3-70B-Instruct,tensor_parallel_size=4,data_parallel_size=2,gpu_memory_utilization=0.8,download_dir=/tmp,enforce_eager=True", "batch_size": 1, "batch_sizes": [], "device": null, "use_cache": null, "limit": null, "bootstrap_iters": 100000, "gen_kwargs": null, "random_seed": 0, "numpy_seed": 1234, "torch_seed": 1234, "fewshot_seed": 1234 }, "git_hash": "788a3672", "date": 1737968180.8770437, "pretty_env_info": "PyTorch version: 2.4.0+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.27.1\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-5.15.0-1064-azure-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.2.128\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\n\nNvidia driver version: 535.161.08\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.4\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 96\nOn-line CPU(s) list: 0-95\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V12 64-Core Processor\nCPU family: 23\nModel: 49\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 0\nBogoMIPS: 4890.88\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru arat umip rdpid\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 3 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 48 MiB (96 instances)\nL3 cache: 384 MiB (24 instances)\nNUMA node(s): 4\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nNUMA node2 CPU(s): 48-71\nNUMA node3 CPU(s): 72-95\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Mitigation; untrained return thunk; SMT disabled\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] onnx==1.14.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\n[pip3] torch-tensorrt==2.0.0.dev0\n[pip3] torchaudio==2.1.0\n[pip3] torchdata==0.7.0a0\n[pip3] torchmetrics==1.2.0\n[pip3] torchvision==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect", "transformers_version": "4.48.1", "upper_git_hash": null, "tokenizer_pad_token": [ "<|finetune_right_pad_id|>", "128004" ], "tokenizer_eos_token": [ "<|eot_id|>", "128009" ], "tokenizer_bos_token": [ "<|begin_of_text|>", "128000" ], "eot_token_id": 128009, "max_length": 131072, "task_hashes": { "mmlu_pro_biology": "78a27f3d4ea386dd0f7b5045f25bf654ba560ee9feac7b22eab763c73b4c37b9", "mmlu_pro_business": "9d10f8702f23d8d5aa9546ebf453e9333a6998a272450bc468b8f74bca8a1824", "mmlu_pro_chemistry": "0e3a8823fed7bd895e42f5053851f12b125f62edfcb36809e4c0aebec80f4506", "mmlu_pro_computer_science": "26e8d9026807a7552684e4ddd1a373873449548e0f0ac8abeada18f32cc5f685", "mmlu_pro_economics": "427580d476e69dc8f095f487f3081cbff1dbfdd3a05a4c13c024ae5bd6907262", "mmlu_pro_engineering": "66bc34b22bf2c19eab04a753e65e8aea2e6834544b27516a6aa2769a9be0b9e5", "mmlu_pro_health": "62edd914028ea5b83013192e458af0d22b843d25ce0ac6e280244d819615cdc4", "mmlu_pro_history": "8295796e4901f2a6b42a2bd8b6e888f2e64ae24ce451f8ecef70db6351f3583d", "mmlu_pro_law": "6969a0ecb6ac565ee29e658094231ddcf1016237aff3d903f5d219dd68a2e5dd", "mmlu_pro_math": "eb48989afd83cb45e2dfd8c769fbe986927de9eb06ac775a7237e939150f20ec", "mmlu_pro_other": "82e12fde3ce84ca4d478ce4623e9dd3877b8bd46c7fc1346c3d9e534df9cbba3", "mmlu_pro_philosophy": "1cd86d5d342a6029560af9a2d51e397df4f537d81d4e6249a0917267c91073e1", "mmlu_pro_physics": "dce786711af6f503b9b1463ca9e245de515859363f4ee7f0aa94656c3357a288", "mmlu_pro_psychology": "526f25dba79a26df39f911b7d6010990c8e21d7c473c89a94e4298566d7cdeda" }, "model_source": "vllm", "model_name": "meta-llama/Llama-3.3-70B-Instruct", "model_name_sanitized": "meta-llama__Llama-3.3-70B-Instruct", "system_instruction": null, "system_instruction_sha": null, "fewshot_as_multiturn": false, "chat_template": null, "chat_template_sha": null, "start_time": 69200.147843926, "end_time": 72294.189406545, "total_evaluation_time_seconds": "3094.041562619008" }