{ "results": { "mmlu_pro": { "exact_match,custom-extract": 0.46725398936170215, "exact_match_stderr,custom-extract": 0.004446206414113066, "alias": "mmlu_pro" }, "mmlu_pro_biology": { "alias": " - biology", "exact_match,custom-extract": 0.6875871687587168, "exact_match_stderr,custom-extract": 0.017320953747153173 }, "mmlu_pro_business": { "alias": " - business", "exact_match,custom-extract": 0.49936628643852976, "exact_match_stderr,custom-extract": 0.01781174819081783 }, "mmlu_pro_chemistry": { "alias": " - chemistry", "exact_match,custom-extract": 0.39752650176678445, "exact_match_stderr,custom-extract": 0.014551933952245952 }, "mmlu_pro_computer_science": { "alias": " - computer_science", "exact_match,custom-extract": 0.5048780487804878, "exact_match_stderr,custom-extract": 0.024722232188886337 }, "mmlu_pro_economics": { "alias": " - economics", "exact_match,custom-extract": 0.6196682464454977, "exact_match_stderr,custom-extract": 0.016720417860194965 }, "mmlu_pro_engineering": { "alias": " - engineering", "exact_match,custom-extract": 0.3323013415892673, "exact_match_stderr,custom-extract": 0.015139747095474023 }, "mmlu_pro_health": { "alias": " - health", "exact_match,custom-extract": 0.511002444987775, "exact_match_stderr,custom-extract": 0.01748855006451323 }, "mmlu_pro_history": { "alias": " - history", "exact_match,custom-extract": 0.4330708661417323, "exact_match_stderr,custom-extract": 0.02541862615034512 }, "mmlu_pro_law": { "alias": " - law", "exact_match,custom-extract": 0.28701180744777477, "exact_match_stderr,custom-extract": 0.01363938247846805 }, "mmlu_pro_math": { "alias": " - math", "exact_match,custom-extract": 0.47964470762398226, "exact_match_stderr,custom-extract": 0.013596994822448527 }, "mmlu_pro_other": { "alias": " - other", "exact_match,custom-extract": 0.44696969696969696, "exact_match_stderr,custom-extract": 0.016364873559887708 }, "mmlu_pro_philosophy": { "alias": " - philosophy", "exact_match,custom-extract": 0.4188376753507014, "exact_match_stderr,custom-extract": 0.022108380221516063 }, "mmlu_pro_physics": { "alias": " - physics", "exact_match,custom-extract": 0.44187836797536567, "exact_match_stderr,custom-extract": 0.0137841011754968 }, "mmlu_pro_psychology": { "alias": " - psychology", "exact_match,custom-extract": 0.6140350877192983, "exact_match_stderr,custom-extract": 0.017244132301501423 } }, "groups": { "mmlu_pro": { "exact_match,custom-extract": 0.46725398936170215, "exact_match_stderr,custom-extract": 0.004446206414113066, "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": "hf", "model_args": "pretrained=tiiuae/Falcon3-7B-Instruct,trust_remote_code=True,cache_dir=/tmp,parallelize=True", "model_num_parameters": 7455550464, "model_dtype": "torch.bfloat16", "model_revision": "main", "model_sha": "5563a370c1848366c7a095bde4bbff2cdb419cc6", "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": "5e10e017", "date": 1736893005.852345, "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": "4.48.0", "upper_git_hash": "f64fe2f2a86055aaecced603b56097fd79201711", "tokenizer_pad_token": [ "<|pad|>", "2023" ], "tokenizer_eos_token": [ "<|endoftext|>", "11" ], "tokenizer_bos_token": [ null, "None" ], "eot_token_id": 11, "max_length": 32768, "task_hashes": { "mmlu_pro_biology": "16c809c3bd9835d58bf3bb74c36233a66ca3d224c1803edea22535e4ce7f4360", "mmlu_pro_business": "c99f593bf18979b611b09ba00bc09ddc3e6b76a9fb1365f10db568ee193ba0c5", "mmlu_pro_chemistry": "a6d38cdf1b84c5029fbe448996bf9fd76a5a927e51232c37746d8412322454cf", "mmlu_pro_computer_science": "de9beede284a884bf478f2f7951055c84310888ba3c289d3bf3f23b8f82ffdbd", "mmlu_pro_economics": "52a942261bdfa4bf43fb807fb973ab258212d3cfddb90fd3cb372792836ec4af", "mmlu_pro_engineering": "0fa251c32b4985125d200a30064e5603a692eedf41c2a3237bf74fed2e4fec50", "mmlu_pro_health": "d57f24fcf156f9faede5cae1af17049dfcbeb85797159cf455c92fe7c12cfc27", "mmlu_pro_history": "5647ea5af92de86f57a6349d9373b236002e27846d989e47401718df7314761b", "mmlu_pro_law": "139898ce0780bc8c88459432881047531e551058c5de9a2d7d412ce3329f453c", "mmlu_pro_math": "813806899ea8b2e09dadefc338b26fbd8ae32cdd17737f0f2453edf83fb40506", "mmlu_pro_other": "cf7b99863728afeacc66b0ed950bf83b9e4d282d7f431a57a96afe4347f2a074", "mmlu_pro_philosophy": "d508069b7725cb21a85aeb05142545ab9a466aaba25a8fe6d42d043835f5da99", "mmlu_pro_physics": "0a0ae7da16f00ff27793e2fc3a379eab1ebc4faa0099fb221a263bdb47f88e00", "mmlu_pro_psychology": "00bc092b5f69c4600e2ae60b25be8af5778d5277c29feece216538d2d67005ba" }, "model_source": "hf", "model_name": "tiiuae/Falcon3-7B-Instruct", "model_name_sanitized": "tiiuae__Falcon3-7B-Instruct", "system_instruction": null, "system_instruction_sha": null, "fewshot_as_multiturn": false, "chat_template": null, "chat_template_sha": null, "start_time": 603256.080151306, "end_time": 607397.753945536, "total_evaluation_time_seconds": "4141.673794229981" }