Qwen2-1.5B-Instruct-quantized.w8a8/results_2024-07-11T04-47-47.568226.json

{
  "results": {
    "openllm": {
      " ": " ",
      "alias": "Open LLM Leaderboard"
    },
    "arc_challenge": {
      "alias": " - arc_challenge",
      "acc,none": 0.4197952218430034,
      "acc_stderr,none": 0.014422181226303026,
      "acc_norm,none": 0.4377133105802048,
      "acc_norm_stderr,none": 0.014497573881108282
    },
    "gsm8k": {
      "alias": " - gsm8k",
      "exact_match,strict-match": 0.5261561789234268,
      "exact_match_stderr,strict-match": 0.013753627037255045,
      "exact_match,flexible-extract": 0.5322213798332069,
      "exact_match_stderr,flexible-extract": 0.013743857303073788
    },
    "hellaswag": {
      "alias": " - hellaswag",
      "acc,none": 0.49213304122684726,
      "acc_stderr,none": 0.004989163747650767,
      "acc_norm,none": 0.6693885680143398,
      "acc_norm_stderr,none": 0.004694718918225724
    },
    "mmlu": {
      "acc,none": 0.5546218487394958,
      "acc_stderr,none": 0.004024375605409149,
      "alias": " - mmlu"
    },
    "mmlu_humanities": {
      "acc,none": 0.5113708820403826,
      "acc_stderr,none": 0.0069581232329427815,
      "alias": "  - humanities"
    },
    "mmlu_formal_logic": {
      "alias": "   - formal_logic",
      "acc,none": 0.3492063492063492,
      "acc_stderr,none": 0.04263906892795132
    },
    "mmlu_high_school_european_history": {
      "alias": "   - high_school_european_history",
      "acc,none": 0.7090909090909091,
      "acc_stderr,none": 0.03546563019624336
    },
    "mmlu_high_school_us_history": {
      "alias": "   - high_school_us_history",
      "acc,none": 0.6421568627450981,
      "acc_stderr,none": 0.03364487286088299
    },
    "mmlu_high_school_world_history": {
      "alias": "   - high_school_world_history",
      "acc,none": 0.729957805907173,
      "acc_stderr,none": 0.028900721906293426
    },
    "mmlu_international_law": {
      "alias": "   - international_law",
      "acc,none": 0.7272727272727273,
      "acc_stderr,none": 0.04065578140908706
    },
    "mmlu_jurisprudence": {
      "alias": "   - jurisprudence",
      "acc,none": 0.7222222222222222,
      "acc_stderr,none": 0.04330043749650742
    },
    "mmlu_logical_fallacies": {
      "alias": "   - logical_fallacies",
      "acc,none": 0.7177914110429447,
      "acc_stderr,none": 0.03536117886664743
    },
    "mmlu_moral_disputes": {
      "alias": "   - moral_disputes",
      "acc,none": 0.6329479768786127,
      "acc_stderr,none": 0.02595005433765408
    },
    "mmlu_moral_scenarios": {
      "alias": "   - moral_scenarios",
      "acc,none": 0.29832402234636873,
      "acc_stderr,none": 0.015301840045129276
    },
    "mmlu_philosophy": {
      "alias": "   - philosophy",
      "acc,none": 0.6012861736334405,
      "acc_stderr,none": 0.027809322585774496
    },
    "mmlu_prehistory": {
      "alias": "   - prehistory",
      "acc,none": 0.5833333333333334,
      "acc_stderr,none": 0.02743162372241501
    },
    "mmlu_professional_law": {
      "alias": "   - professional_law",
      "acc,none": 0.439374185136897,
      "acc_stderr,none": 0.012676014778580217
    },
    "mmlu_world_religions": {
      "alias": "   - world_religions",
      "acc,none": 0.7134502923976608,
      "acc_stderr,none": 0.03467826685703826
    },
    "mmlu_other": {
      "acc,none": 0.6073382684261346,
      "acc_stderr,none": 0.00843520557334809,
      "alias": "  - other"
    },
    "mmlu_business_ethics": {
      "alias": "   - business_ethics",
      "acc,none": 0.62,
      "acc_stderr,none": 0.048783173121456316
    },
    "mmlu_clinical_knowledge": {
      "alias": "   - clinical_knowledge",
      "acc,none": 0.5773584905660377,
      "acc_stderr,none": 0.03040233144576954
    },
    "mmlu_college_medicine": {
      "alias": "   - college_medicine",
      "acc,none": 0.5375722543352601,
      "acc_stderr,none": 0.0380168510452446
    },
    "mmlu_global_facts": {
      "alias": "   - global_facts",
      "acc,none": 0.27,
      "acc_stderr,none": 0.044619604333847394
    },
    "mmlu_human_aging": {
      "alias": "   - human_aging",
      "acc,none": 0.6233183856502242,
      "acc_stderr,none": 0.032521134899291884
    },
    "mmlu_management": {
      "alias": "   - management",
      "acc,none": 0.7961165048543689,
      "acc_stderr,none": 0.039891398595317706
    },
    "mmlu_marketing": {
      "alias": "   - marketing",
      "acc,none": 0.8418803418803419,
      "acc_stderr,none": 0.023902325549560406
    },
    "mmlu_medical_genetics": {
      "alias": "   - medical_genetics",
      "acc,none": 0.62,
      "acc_stderr,none": 0.048783173121456316
    },
    "mmlu_miscellaneous": {
      "alias": "   - miscellaneous",
      "acc,none": 0.7011494252873564,
      "acc_stderr,none": 0.016369256815093124
    },
    "mmlu_nutrition": {
      "alias": "   - nutrition",
      "acc,none": 0.6699346405228758,
      "acc_stderr,none": 0.026925654653615703
    },
    "mmlu_professional_accounting": {
      "alias": "   - professional_accounting",
      "acc,none": 0.43617021276595747,
      "acc_stderr,none": 0.02958345203628407
    },
    "mmlu_professional_medicine": {
      "alias": "   - professional_medicine",
      "acc,none": 0.44485294117647056,
      "acc_stderr,none": 0.030187532060329387
    },
    "mmlu_virology": {
      "alias": "   - virology",
      "acc,none": 0.4457831325301205,
      "acc_stderr,none": 0.038695433234721015
    },
    "mmlu_social_sciences": {
      "acc,none": 0.6460838479038024,
      "acc_stderr,none": 0.008424637910866435,
      "alias": "  - social sciences"
    },
    "mmlu_econometrics": {
      "alias": "   - econometrics",
      "acc,none": 0.34210526315789475,
      "acc_stderr,none": 0.04462917535336936
    },
    "mmlu_high_school_geography": {
      "alias": "   - high_school_geography",
      "acc,none": 0.7373737373737373,
      "acc_stderr,none": 0.03135305009533087
    },
    "mmlu_high_school_government_and_politics": {
      "alias": "   - high_school_government_and_politics",
      "acc,none": 0.772020725388601,
      "acc_stderr,none": 0.030276909945178267
    },
    "mmlu_high_school_macroeconomics": {
      "alias": "   - high_school_macroeconomics",
      "acc,none": 0.5743589743589743,
      "acc_stderr,none": 0.025069094387296535
    },
    "mmlu_high_school_microeconomics": {
      "alias": "   - high_school_microeconomics",
      "acc,none": 0.5840336134453782,
      "acc_stderr,none": 0.03201650100739612
    },
    "mmlu_high_school_psychology": {
      "alias": "   - high_school_psychology",
      "acc,none": 0.7394495412844037,
      "acc_stderr,none": 0.018819182034850068
    },
    "mmlu_human_sexuality": {
      "alias": "   - human_sexuality",
      "acc,none": 0.6870229007633588,
      "acc_stderr,none": 0.04066962905677697
    },
    "mmlu_professional_psychology": {
      "alias": "   - professional_psychology",
      "acc,none": 0.5506535947712419,
      "acc_stderr,none": 0.020123766528027266
    },
    "mmlu_public_relations": {
      "alias": "   - public_relations",
      "acc,none": 0.5545454545454546,
      "acc_stderr,none": 0.047605488214603246
    },
    "mmlu_security_studies": {
      "alias": "   - security_studies",
      "acc,none": 0.7061224489795919,
      "acc_stderr,none": 0.029162738410249755
    },
    "mmlu_sociology": {
      "alias": "   - sociology",
      "acc,none": 0.736318407960199,
      "acc_stderr,none": 0.031157150869355554
    },
    "mmlu_us_foreign_policy": {
      "alias": "   - us_foreign_policy",
      "acc,none": 0.79,
      "acc_stderr,none": 0.040936018074033256
    },
    "mmlu_stem": {
      "acc,none": 0.4779575007928957,
      "acc_stderr,none": 0.00875998268314062,
      "alias": "  - stem"
    },
    "mmlu_abstract_algebra": {
      "alias": "   - abstract_algebra",
      "acc,none": 0.34,
      "acc_stderr,none": 0.04760952285695236
    },
    "mmlu_anatomy": {
      "alias": "   - anatomy",
      "acc,none": 0.5259259259259259,
      "acc_stderr,none": 0.04313531696750574
    },
    "mmlu_astronomy": {
      "alias": "   - astronomy",
      "acc,none": 0.5592105263157895,
      "acc_stderr,none": 0.04040311062490436
    },
    "mmlu_college_biology": {
      "alias": "   - college_biology",
      "acc,none": 0.5763888888888888,
      "acc_stderr,none": 0.0413212501972337
    },
    "mmlu_college_chemistry": {
      "alias": "   - college_chemistry",
      "acc,none": 0.41,
      "acc_stderr,none": 0.04943110704237102
    },
    "mmlu_college_computer_science": {
      "alias": "   - college_computer_science",
      "acc,none": 0.48,
      "acc_stderr,none": 0.050211673156867795
    },
    "mmlu_college_mathematics": {
      "alias": "   - college_mathematics",
      "acc,none": 0.3,
      "acc_stderr,none": 0.046056618647183814
    },
    "mmlu_college_physics": {
      "alias": "   - college_physics",
      "acc,none": 0.37254901960784315,
      "acc_stderr,none": 0.048108401480826346
    },
    "mmlu_computer_security": {
      "alias": "   - computer_security",
      "acc,none": 0.66,
      "acc_stderr,none": 0.04760952285695237
    },
    "mmlu_conceptual_physics": {
      "alias": "   - conceptual_physics",
      "acc,none": 0.48936170212765956,
      "acc_stderr,none": 0.03267862331014063
    },
    "mmlu_electrical_engineering": {
      "alias": "   - electrical_engineering",
      "acc,none": 0.5586206896551724,
      "acc_stderr,none": 0.04137931034482757
    },
    "mmlu_elementary_mathematics": {
      "alias": "   - elementary_mathematics",
      "acc,none": 0.4365079365079365,
      "acc_stderr,none": 0.02554284681740051
    },
    "mmlu_high_school_biology": {
      "alias": "   - high_school_biology",
      "acc,none": 0.635483870967742,
      "acc_stderr,none": 0.02737987122994324
    },
    "mmlu_high_school_chemistry": {
      "alias": "   - high_school_chemistry",
      "acc,none": 0.49261083743842365,
      "acc_stderr,none": 0.03517603540361008
    },
    "mmlu_high_school_computer_science": {
      "alias": "   - high_school_computer_science",
      "acc,none": 0.54,
      "acc_stderr,none": 0.05009082659620332
    },
    "mmlu_high_school_mathematics": {
      "alias": "   - high_school_mathematics",
      "acc,none": 0.3851851851851852,
      "acc_stderr,none": 0.029670906124630882
    },
    "mmlu_high_school_physics": {
      "alias": "   - high_school_physics",
      "acc,none": 0.33112582781456956,
      "acc_stderr,none": 0.038425817186598696
    },
    "mmlu_high_school_statistics": {
      "alias": "   - high_school_statistics",
      "acc,none": 0.4398148148148148,
      "acc_stderr,none": 0.0338517797604481
    },
    "mmlu_machine_learning": {
      "alias": "   - machine_learning",
      "acc,none": 0.44642857142857145,
      "acc_stderr,none": 0.04718471485219588
    },
    "truthfulqa_gen": {
      "alias": " - truthfulqa_gen",
      "bleu_max,none": 16.725449543518508,
      "bleu_max_stderr,none": 0.6765140252338542,
      "bleu_acc,none": 0.33659730722154224,
      "bleu_acc_stderr,none": 0.016542412809494905,
      "bleu_diff,none": -3.0595350171646474,
      "bleu_diff_stderr,none": 0.5251806518877615,
      "rouge1_max,none": 39.10695233341565,
      "rouge1_max_stderr,none": 0.8831073941646559,
      "rouge1_acc,none": 0.31946144430844553,
      "rouge1_acc_stderr,none": 0.016322644182960498,
      "rouge1_diff,none": -8.079420619108062,
      "rouge1_diff_stderr,none": 0.8088876413193208,
      "rouge2_max,none": 23.00635625872184,
      "rouge2_max_stderr,none": 0.896942078080225,
      "rouge2_acc,none": 0.23623011015911874,
      "rouge2_acc_stderr,none": 0.014869755015871103,
      "rouge2_diff,none": -6.409056020143248,
      "rouge2_diff_stderr,none": 0.7784892413485867,
      "rougeL_max,none": 36.15572879947301,
      "rougeL_max_stderr,none": 0.8714944272677017,
      "rougeL_acc,none": 0.3023255813953488,
      "rougeL_acc_stderr,none": 0.016077509266133033,
      "rougeL_diff,none": -8.280613882921907,
      "rougeL_diff_stderr,none": 0.8084496219992774
    },
    "truthfulqa_mc1": {
      "alias": " - truthfulqa_mc1",
      "acc,none": 0.2778457772337821,
      "acc_stderr,none": 0.015680929364024654
    },
    "truthfulqa_mc2": {
      "alias": " - truthfulqa_mc2",
      "acc,none": 0.4325389537747215,
      "acc_stderr,none": 0.014428673995726733
    },
    "winogrande": {
      "alias": " - winogrande",
      "acc,none": 0.6582478295185478,
      "acc_stderr,none": 0.013330103018622847
    }
  },
  "groups": {
    "mmlu": {
      "acc,none": 0.5546218487394958,
      "acc_stderr,none": 0.004024375605409149,
      "alias": " - mmlu"
    },
    "mmlu_humanities": {
      "acc,none": 0.5113708820403826,
      "acc_stderr,none": 0.0069581232329427815,
      "alias": "  - humanities"
    },
    "mmlu_other": {
      "acc,none": 0.6073382684261346,
      "acc_stderr,none": 0.00843520557334809,
      "alias": "  - other"
    },
    "mmlu_social_sciences": {
      "acc,none": 0.6460838479038024,
      "acc_stderr,none": 0.008424637910866435,
      "alias": "  - social sciences"
    },
    "mmlu_stem": {
      "acc,none": 0.4779575007928957,
      "acc_stderr,none": 0.00875998268314062,
      "alias": "  - stem"
    }
  },
  "group_subtasks": {
    "mmlu_humanities": [
      "mmlu_prehistory",
      "mmlu_moral_scenarios",
      "mmlu_moral_disputes",
      "mmlu_philosophy",
      "mmlu_high_school_world_history",
      "mmlu_logical_fallacies",
      "mmlu_formal_logic",
      "mmlu_high_school_us_history",
      "mmlu_high_school_european_history",
      "mmlu_professional_law",
      "mmlu_jurisprudence",
      "mmlu_international_law",
      "mmlu_world_religions"
    ],
    "mmlu_social_sciences": [
      "mmlu_high_school_psychology",
      "mmlu_us_foreign_policy",
      "mmlu_professional_psychology",
      "mmlu_human_sexuality",
      "mmlu_public_relations",
      "mmlu_security_studies",
      "mmlu_sociology",
      "mmlu_high_school_microeconomics",
      "mmlu_high_school_macroeconomics",
      "mmlu_high_school_government_and_politics",
      "mmlu_econometrics",
      "mmlu_high_school_geography"
    ],
    "mmlu_other": [
      "mmlu_professional_accounting",
      "mmlu_global_facts",
      "mmlu_college_medicine",
      "mmlu_virology",
      "mmlu_human_aging",
      "mmlu_professional_medicine",
      "mmlu_medical_genetics",
      "mmlu_business_ethics",
      "mmlu_management",
      "mmlu_miscellaneous",
      "mmlu_nutrition",
      "mmlu_marketing",
      "mmlu_clinical_knowledge"
    ],
    "mmlu_stem": [
      "mmlu_anatomy",
      "mmlu_college_physics",
      "mmlu_computer_security",
      "mmlu_conceptual_physics",
      "mmlu_high_school_chemistry",
      "mmlu_college_computer_science",
      "mmlu_high_school_biology",
      "mmlu_high_school_mathematics",
      "mmlu_high_school_statistics",
      "mmlu_college_chemistry",
      "mmlu_electrical_engineering",
      "mmlu_college_mathematics",
      "mmlu_college_biology",
      "mmlu_abstract_algebra",
      "mmlu_elementary_mathematics",
      "mmlu_high_school_physics",
      "mmlu_high_school_computer_science",
      "mmlu_machine_learning",
      "mmlu_astronomy"
    ],
    "mmlu": [
      "mmlu_stem",
      "mmlu_other",
      "mmlu_social_sciences",
      "mmlu_humanities"
    ],
    "openllm": [
      "arc_challenge",
      "hellaswag",
      "truthfulqa_mc2",
      "truthfulqa_mc1",
      "truthfulqa_gen",
      "mmlu",
      "winogrande",
      "gsm8k"
    ]
  },
  "configs": {
    "arc_challenge": {
      "task": "arc_challenge",
      "tag": [
        "ai2_arc"
      ],
      "dataset_path": "allenai/ai2_arc",
      "dataset_name": "ARC-Challenge",
      "training_split": "train",
      "validation_split": "validation",
      "test_split": "test",
      "fewshot_split": "validation",
      "doc_to_text": "Question: {{question}}\nAnswer:",
      "doc_to_target": "{{choices.label.index(answerKey)}}",
      "doc_to_choice": "{{choices.text}}",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 25,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "acc_norm",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": true,
      "doc_to_decontamination_query": "Question: {{question}}\nAnswer:",
      "metadata": {
        "version": 1.0
      }
    },
    "gsm8k": {
      "task": "gsm8k",
      "tag": [
        "math_word_problems"
      ],
      "dataset_path": "gsm8k",
      "dataset_name": "main",
      "training_split": "train",
      "test_split": "test",
      "fewshot_split": "train",
      "doc_to_text": "Question: {{question}}\nAnswer:",
      "doc_to_target": "{{answer}}",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "exact_match",
          "aggregation": "mean",
          "higher_is_better": true,
          "ignore_case": true,
          "ignore_punctuation": false,
          "regexes_to_ignore": [
            ",",
            "\\$",
            "(?s).*#### ",
            "\\.$"
          ]
        }
      ],
      "output_type": "generate_until",
      "generation_kwargs": {
        "until": [
          "Question:",
          "</s>",
          "<|im_end|>"
        ],
        "do_sample": false,
        "temperature": 0.0
      },
      "repeats": 1,
      "filter_list": [
        {
          "name": "strict-match",
          "filter": [
            {
              "function": "regex",
              "regex_pattern": "#### (\\-?[0-9\\.\\,]+)"
            },
            {
              "function": "take_first"
            }
          ]
        },
        {
          "name": "flexible-extract",
          "filter": [
            {
              "function": "regex",
              "group_select": -1,
              "regex_pattern": "(-?[$0-9.,]{2,})|(-?[0-9]+)"
            },
            {
              "function": "take_first"
            }
          ]
        }
      ],
      "should_decontaminate": false,
      "metadata": {
        "version": 3.0
      }
    },
    "hellaswag": {
      "task": "hellaswag",
      "tag": [
        "multiple_choice"
      ],
      "dataset_path": "hellaswag",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "training_split": "train",
      "validation_split": "validation",
      "fewshot_split": "train",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_doc(doc):\n        ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n        out_doc = {\n            \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n            \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n            \"gold\": int(doc[\"label\"]),\n        }\n        return out_doc\n\n    return dataset.map(_process_doc)\n",
      "doc_to_text": "{{query}}",
      "doc_to_target": "{{label}}",
      "doc_to_choice": "choices",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 10,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "acc_norm",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 1.0
      }
    },
    "mmlu_abstract_algebra": {
      "task": "mmlu_abstract_algebra",
      "task_alias": "abstract_algebra",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "abstract_algebra",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about abstract algebra.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_anatomy": {
      "task": "mmlu_anatomy",
      "task_alias": "anatomy",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "anatomy",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about anatomy.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_astronomy": {
      "task": "mmlu_astronomy",
      "task_alias": "astronomy",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "astronomy",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about astronomy.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_business_ethics": {
      "task": "mmlu_business_ethics",
      "task_alias": "business_ethics",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "business_ethics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about business ethics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_clinical_knowledge": {
      "task": "mmlu_clinical_knowledge",
      "task_alias": "clinical_knowledge",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "clinical_knowledge",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about clinical knowledge.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_college_biology": {
      "task": "mmlu_college_biology",
      "task_alias": "college_biology",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "college_biology",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about college biology.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_college_chemistry": {
      "task": "mmlu_college_chemistry",
      "task_alias": "college_chemistry",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "college_chemistry",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about college chemistry.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_college_computer_science": {
      "task": "mmlu_college_computer_science",
      "task_alias": "college_computer_science",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "college_computer_science",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about college computer science.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_college_mathematics": {
      "task": "mmlu_college_mathematics",
      "task_alias": "college_mathematics",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "college_mathematics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about college mathematics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_college_medicine": {
      "task": "mmlu_college_medicine",
      "task_alias": "college_medicine",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "college_medicine",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about college medicine.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_college_physics": {
      "task": "mmlu_college_physics",
      "task_alias": "college_physics",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "college_physics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about college physics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_computer_security": {
      "task": "mmlu_computer_security",
      "task_alias": "computer_security",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "computer_security",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about computer security.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_conceptual_physics": {
      "task": "mmlu_conceptual_physics",
      "task_alias": "conceptual_physics",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "conceptual_physics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about conceptual physics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_econometrics": {
      "task": "mmlu_econometrics",
      "task_alias": "econometrics",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "econometrics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about econometrics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_electrical_engineering": {
      "task": "mmlu_electrical_engineering",
      "task_alias": "electrical_engineering",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "electrical_engineering",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about electrical engineering.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_elementary_mathematics": {
      "task": "mmlu_elementary_mathematics",
      "task_alias": "elementary_mathematics",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "elementary_mathematics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about elementary mathematics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_formal_logic": {
      "task": "mmlu_formal_logic",
      "task_alias": "formal_logic",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "formal_logic",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about formal logic.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_global_facts": {
      "task": "mmlu_global_facts",
      "task_alias": "global_facts",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "global_facts",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about global facts.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_biology": {
      "task": "mmlu_high_school_biology",
      "task_alias": "high_school_biology",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_biology",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school biology.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_chemistry": {
      "task": "mmlu_high_school_chemistry",
      "task_alias": "high_school_chemistry",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_chemistry",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school chemistry.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_computer_science": {
      "task": "mmlu_high_school_computer_science",
      "task_alias": "high_school_computer_science",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_computer_science",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school computer science.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_european_history": {
      "task": "mmlu_high_school_european_history",
      "task_alias": "high_school_european_history",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_european_history",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school european history.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_geography": {
      "task": "mmlu_high_school_geography",
      "task_alias": "high_school_geography",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_geography",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school geography.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_government_and_politics": {
      "task": "mmlu_high_school_government_and_politics",
      "task_alias": "high_school_government_and_politics",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_government_and_politics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school government and politics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_macroeconomics": {
      "task": "mmlu_high_school_macroeconomics",
      "task_alias": "high_school_macroeconomics",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_macroeconomics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school macroeconomics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_mathematics": {
      "task": "mmlu_high_school_mathematics",
      "task_alias": "high_school_mathematics",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_mathematics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school mathematics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_microeconomics": {
      "task": "mmlu_high_school_microeconomics",
      "task_alias": "high_school_microeconomics",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_microeconomics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school microeconomics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_physics": {
      "task": "mmlu_high_school_physics",
      "task_alias": "high_school_physics",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_physics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school physics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_psychology": {
      "task": "mmlu_high_school_psychology",
      "task_alias": "high_school_psychology",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_psychology",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school psychology.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_statistics": {
      "task": "mmlu_high_school_statistics",
      "task_alias": "high_school_statistics",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_statistics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school statistics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_us_history": {
      "task": "mmlu_high_school_us_history",
      "task_alias": "high_school_us_history",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_us_history",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school us history.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_high_school_world_history": {
      "task": "mmlu_high_school_world_history",
      "task_alias": "high_school_world_history",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "high_school_world_history",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about high school world history.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_human_aging": {
      "task": "mmlu_human_aging",
      "task_alias": "human_aging",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "human_aging",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about human aging.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_human_sexuality": {
      "task": "mmlu_human_sexuality",
      "task_alias": "human_sexuality",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "human_sexuality",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about human sexuality.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_international_law": {
      "task": "mmlu_international_law",
      "task_alias": "international_law",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "international_law",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about international law.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_jurisprudence": {
      "task": "mmlu_jurisprudence",
      "task_alias": "jurisprudence",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "jurisprudence",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about jurisprudence.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_logical_fallacies": {
      "task": "mmlu_logical_fallacies",
      "task_alias": "logical_fallacies",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "logical_fallacies",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about logical fallacies.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_machine_learning": {
      "task": "mmlu_machine_learning",
      "task_alias": "machine_learning",
      "tag": "mmlu_stem_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "machine_learning",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about machine learning.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_management": {
      "task": "mmlu_management",
      "task_alias": "management",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "management",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about management.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_marketing": {
      "task": "mmlu_marketing",
      "task_alias": "marketing",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "marketing",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about marketing.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_medical_genetics": {
      "task": "mmlu_medical_genetics",
      "task_alias": "medical_genetics",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "medical_genetics",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about medical genetics.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_miscellaneous": {
      "task": "mmlu_miscellaneous",
      "task_alias": "miscellaneous",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "miscellaneous",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about miscellaneous.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_moral_disputes": {
      "task": "mmlu_moral_disputes",
      "task_alias": "moral_disputes",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "moral_disputes",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about moral disputes.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_moral_scenarios": {
      "task": "mmlu_moral_scenarios",
      "task_alias": "moral_scenarios",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "moral_scenarios",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about moral scenarios.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_nutrition": {
      "task": "mmlu_nutrition",
      "task_alias": "nutrition",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "nutrition",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about nutrition.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_philosophy": {
      "task": "mmlu_philosophy",
      "task_alias": "philosophy",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "philosophy",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about philosophy.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_prehistory": {
      "task": "mmlu_prehistory",
      "task_alias": "prehistory",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "prehistory",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about prehistory.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_professional_accounting": {
      "task": "mmlu_professional_accounting",
      "task_alias": "professional_accounting",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "professional_accounting",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about professional accounting.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_professional_law": {
      "task": "mmlu_professional_law",
      "task_alias": "professional_law",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "professional_law",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about professional law.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_professional_medicine": {
      "task": "mmlu_professional_medicine",
      "task_alias": "professional_medicine",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "professional_medicine",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about professional medicine.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_professional_psychology": {
      "task": "mmlu_professional_psychology",
      "task_alias": "professional_psychology",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "professional_psychology",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about professional psychology.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_public_relations": {
      "task": "mmlu_public_relations",
      "task_alias": "public_relations",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "public_relations",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about public relations.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_security_studies": {
      "task": "mmlu_security_studies",
      "task_alias": "security_studies",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "security_studies",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about security studies.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_sociology": {
      "task": "mmlu_sociology",
      "task_alias": "sociology",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "sociology",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about sociology.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_us_foreign_policy": {
      "task": "mmlu_us_foreign_policy",
      "task_alias": "us_foreign_policy",
      "tag": "mmlu_social_sciences_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "us_foreign_policy",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about us foreign policy.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_virology": {
      "task": "mmlu_virology",
      "task_alias": "virology",
      "tag": "mmlu_other_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "virology",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about virology.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "mmlu_world_religions": {
      "task": "mmlu_world_religions",
      "task_alias": "world_religions",
      "tag": "mmlu_humanities_tasks",
      "dataset_path": "hails/mmlu_no_train",
      "dataset_name": "world_religions",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "test_split": "test",
      "fewshot_split": "dev",
      "doc_to_text": "{{question.strip()}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nAnswer:",
      "doc_to_target": "answer",
      "doc_to_choice": [
        "A",
        "B",
        "C",
        "D"
      ],
      "description": "The following are multiple choice questions (with answers) about world religions.\n\n",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n"
      },
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 0.0
      }
    },
    "truthfulqa_gen": {
      "task": "truthfulqa_gen",
      "tag": [
        "truthfulqa"
      ],
      "dataset_path": "truthful_qa",
      "dataset_name": "generation",
      "validation_split": "validation",
      "process_docs": "def process_docs_gen(dataset: datasets.Dataset) -> datasets.Dataset:\n    return dataset.map(preprocess_function)\n",
      "doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question}}",
      "doc_to_target": " ",
      "process_results": "def process_results_gen(doc, results):\n    completion = results[0]\n    true_refs, false_refs = doc[\"correct_answers\"], doc[\"incorrect_answers\"]\n    all_refs = true_refs + false_refs\n\n    # Process the sentence-level BLEURT, BLEU, and ROUGE for similarity measures.\n\n    # # BLEURT\n    # bleurt_scores_true = self.bleurt.compute(\n    #     predictions=[completion] * len(true_refs), references=true_refs\n    # )[\"scores\"]\n    # bleurt_scores_false = self.bleurt.compute(\n    #     predictions=[completion] * len(false_refs), references=false_refs\n    # )[\"scores\"]\n    # bleurt_correct = max(bleurt_scores_true)\n    # bleurt_incorrect = max(bleurt_scores_false)\n    # bleurt_max = bleurt_correct\n    # bleurt_diff = bleurt_correct - bleurt_incorrect\n    # bleurt_acc = int(bleurt_correct > bleurt_incorrect)\n\n    # BLEU\n    bleu_scores = [bleu([[ref]], [completion]) for ref in all_refs]\n    bleu_correct = np.nanmax(bleu_scores[: len(true_refs)])\n    bleu_incorrect = np.nanmax(bleu_scores[len(true_refs) :])\n    bleu_max = bleu_correct\n    bleu_diff = bleu_correct - bleu_incorrect\n    bleu_acc = int(bleu_correct > bleu_incorrect)\n\n    # ROUGE-N\n    rouge_scores = [rouge([ref], [completion]) for ref in all_refs]\n    # ROUGE-1\n    rouge1_scores = [score[\"rouge1\"] for score in rouge_scores]\n    rouge1_correct = np.nanmax(rouge1_scores[: len(true_refs)])\n    rouge1_incorrect = np.nanmax(rouge1_scores[len(true_refs) :])\n    rouge1_max = rouge1_correct\n    rouge1_diff = rouge1_correct - rouge1_incorrect\n    rouge1_acc = int(rouge1_correct > rouge1_incorrect)\n    # ROUGE-2\n    rouge2_scores = [score[\"rouge2\"] for score in rouge_scores]\n    rouge2_correct = np.nanmax(rouge2_scores[: len(true_refs)])\n    rouge2_incorrect = np.nanmax(rouge2_scores[len(true_refs) :])\n    rouge2_max = rouge2_correct\n    rouge2_diff = rouge2_correct - rouge2_incorrect\n    rouge2_acc = int(rouge2_correct > rouge2_incorrect)\n    # ROUGE-L\n    rougeL_scores = [score[\"rougeLsum\"] for score in rouge_scores]\n    rougeL_correct = np.nanmax(rougeL_scores[: len(true_refs)])\n    rougeL_incorrect = np.nanmax(rougeL_scores[len(true_refs) :])\n    rougeL_max = rougeL_correct\n    rougeL_diff = rougeL_correct - rougeL_incorrect\n    rougeL_acc = int(rougeL_correct > rougeL_incorrect)\n\n    return {\n        # \"bleurt_max\": bleurt_max,\n        # \"bleurt_acc\": bleurt_acc,\n        # \"bleurt_diff\": bleurt_diff,\n        \"bleu_max\": bleu_max,\n        \"bleu_acc\": bleu_acc,\n        \"bleu_diff\": bleu_diff,\n        \"rouge1_max\": rouge1_max,\n        \"rouge1_acc\": rouge1_acc,\n        \"rouge1_diff\": rouge1_diff,\n        \"rouge2_max\": rouge2_max,\n        \"rouge2_acc\": rouge2_acc,\n        \"rouge2_diff\": rouge2_diff,\n        \"rougeL_max\": rougeL_max,\n        \"rougeL_acc\": rougeL_acc,\n        \"rougeL_diff\": rougeL_diff,\n    }\n",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 0,
      "metric_list": [
        {
          "metric": "bleu_max",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "bleu_acc",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "bleu_diff",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rouge1_max",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rouge1_acc",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rouge1_diff",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rouge2_max",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rouge2_acc",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rouge2_diff",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rougeL_max",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rougeL_acc",
          "aggregation": "mean",
          "higher_is_better": true
        },
        {
          "metric": "rougeL_diff",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "generate_until",
      "generation_kwargs": {
        "until": [
          "\n\n"
        ],
        "do_sample": false
      },
      "repeats": 1,
      "should_decontaminate": true,
      "doc_to_decontamination_query": "question",
      "metadata": {
        "version": 3.0
      }
    },
    "truthfulqa_mc1": {
      "task": "truthfulqa_mc1",
      "tag": [
        "truthfulqa"
      ],
      "dataset_path": "truthful_qa",
      "dataset_name": "multiple_choice",
      "validation_split": "validation",
      "doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question + '\nA:'}}",
      "doc_to_target": 0,
      "doc_to_choice": "{{mc1_targets.choices}}",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 0,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": true,
      "doc_to_decontamination_query": "question",
      "metadata": {
        "version": 2.0
      }
    },
    "truthfulqa_mc2": {
      "task": "truthfulqa_mc2",
      "tag": [
        "truthfulqa"
      ],
      "dataset_path": "truthful_qa",
      "dataset_name": "multiple_choice",
      "validation_split": "validation",
      "doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question + '\nA:'}}",
      "doc_to_target": 0,
      "doc_to_choice": "{{mc2_targets.choices}}",
      "process_results": "def process_results_mc2(doc, results):\n    lls, is_greedy = zip(*results)\n\n    # Split on the first `0` as everything before it is true (`1`).\n    split_idx = list(doc[\"mc2_targets\"][\"labels\"]).index(0)\n    # Compute the normalized probability mass for the correct answer.\n    ll_true, ll_false = lls[:split_idx], lls[split_idx:]\n    p_true, p_false = np.exp(np.array(ll_true)), np.exp(np.array(ll_false))\n    p_true = p_true / (sum(p_true) + sum(p_false))\n\n    return {\"acc\": sum(p_true)}\n",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 0,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": true,
      "doc_to_decontamination_query": "question",
      "metadata": {
        "version": 2.0
      }
    },
    "winogrande": {
      "task": "winogrande",
      "dataset_path": "winogrande",
      "dataset_name": "winogrande_xl",
      "training_split": "train",
      "validation_split": "validation",
      "fewshot_split": "train",
      "doc_to_text": "def doc_to_text(doc):\n    answer_to_num = {\"1\": 0, \"2\": 1}\n    return answer_to_num[doc[\"answer\"]]\n",
      "doc_to_target": "def doc_to_target(doc):\n    idx = doc[\"sentence\"].index(\"_\") + 1\n    return doc[\"sentence\"][idx:].strip()\n",
      "doc_to_choice": "def doc_to_choice(doc):\n    idx = doc[\"sentence\"].index(\"_\")\n    options = [doc[\"option1\"], doc[\"option2\"]]\n    return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 5,
      "metric_list": [
        {
          "metric": "acc",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": true,
      "doc_to_decontamination_query": "sentence",
      "metadata": {
        "version": 1.0
      }
    }
  },
  "versions": {
    "arc_challenge": 1.0,
    "gsm8k": 3.0,
    "hellaswag": 1.0,
    "mmlu": 1,
    "mmlu_abstract_algebra": 0.0,
    "mmlu_anatomy": 0.0,
    "mmlu_astronomy": 0.0,
    "mmlu_business_ethics": 0.0,
    "mmlu_clinical_knowledge": 0.0,
    "mmlu_college_biology": 0.0,
    "mmlu_college_chemistry": 0.0,
    "mmlu_college_computer_science": 0.0,
    "mmlu_college_mathematics": 0.0,
    "mmlu_college_medicine": 0.0,
    "mmlu_college_physics": 0.0,
    "mmlu_computer_security": 0.0,
    "mmlu_conceptual_physics": 0.0,
    "mmlu_econometrics": 0.0,
    "mmlu_electrical_engineering": 0.0,
    "mmlu_elementary_mathematics": 0.0,
    "mmlu_formal_logic": 0.0,
    "mmlu_global_facts": 0.0,
    "mmlu_high_school_biology": 0.0,
    "mmlu_high_school_chemistry": 0.0,
    "mmlu_high_school_computer_science": 0.0,
    "mmlu_high_school_european_history": 0.0,
    "mmlu_high_school_geography": 0.0,
    "mmlu_high_school_government_and_politics": 0.0,
    "mmlu_high_school_macroeconomics": 0.0,
    "mmlu_high_school_mathematics": 0.0,
    "mmlu_high_school_microeconomics": 0.0,
    "mmlu_high_school_physics": 0.0,
    "mmlu_high_school_psychology": 0.0,
    "mmlu_high_school_statistics": 0.0,
    "mmlu_high_school_us_history": 0.0,
    "mmlu_high_school_world_history": 0.0,
    "mmlu_human_aging": 0.0,
    "mmlu_human_sexuality": 0.0,
    "mmlu_humanities": 1,
    "mmlu_international_law": 0.0,
    "mmlu_jurisprudence": 0.0,
    "mmlu_logical_fallacies": 0.0,
    "mmlu_machine_learning": 0.0,
    "mmlu_management": 0.0,
    "mmlu_marketing": 0.0,
    "mmlu_medical_genetics": 0.0,
    "mmlu_miscellaneous": 0.0,
    "mmlu_moral_disputes": 0.0,
    "mmlu_moral_scenarios": 0.0,
    "mmlu_nutrition": 0.0,
    "mmlu_other": 1,
    "mmlu_philosophy": 0.0,
    "mmlu_prehistory": 0.0,
    "mmlu_professional_accounting": 0.0,
    "mmlu_professional_law": 0.0,
    "mmlu_professional_medicine": 0.0,
    "mmlu_professional_psychology": 0.0,
    "mmlu_public_relations": 0.0,
    "mmlu_security_studies": 0.0,
    "mmlu_social_sciences": 1,
    "mmlu_sociology": 0.0,
    "mmlu_stem": 1,
    "mmlu_us_foreign_policy": 0.0,
    "mmlu_virology": 0.0,
    "mmlu_world_religions": 0.0,
    "truthfulqa_gen": 3.0,
    "truthfulqa_mc1": 2.0,
    "truthfulqa_mc2": 2.0,
    "winogrande": 1.0
  },
  "n-shot": {
    "arc_challenge": 25,
    "gsm8k": 5,
    "hellaswag": 10,
    "mmlu_abstract_algebra": 5,
    "mmlu_anatomy": 5,
    "mmlu_astronomy": 5,
    "mmlu_business_ethics": 5,
    "mmlu_clinical_knowledge": 5,
    "mmlu_college_biology": 5,
    "mmlu_college_chemistry": 5,
    "mmlu_college_computer_science": 5,
    "mmlu_college_mathematics": 5,
    "mmlu_college_medicine": 5,
    "mmlu_college_physics": 5,
    "mmlu_computer_security": 5,
    "mmlu_conceptual_physics": 5,
    "mmlu_econometrics": 5,
    "mmlu_electrical_engineering": 5,
    "mmlu_elementary_mathematics": 5,
    "mmlu_formal_logic": 5,
    "mmlu_global_facts": 5,
    "mmlu_high_school_biology": 5,
    "mmlu_high_school_chemistry": 5,
    "mmlu_high_school_computer_science": 5,
    "mmlu_high_school_european_history": 5,
    "mmlu_high_school_geography": 5,
    "mmlu_high_school_government_and_politics": 5,
    "mmlu_high_school_macroeconomics": 5,
    "mmlu_high_school_mathematics": 5,
    "mmlu_high_school_microeconomics": 5,
    "mmlu_high_school_physics": 5,
    "mmlu_high_school_psychology": 5,
    "mmlu_high_school_statistics": 5,
    "mmlu_high_school_us_history": 5,
    "mmlu_high_school_world_history": 5,
    "mmlu_human_aging": 5,
    "mmlu_human_sexuality": 5,
    "mmlu_international_law": 5,
    "mmlu_jurisprudence": 5,
    "mmlu_logical_fallacies": 5,
    "mmlu_machine_learning": 5,
    "mmlu_management": 5,
    "mmlu_marketing": 5,
    "mmlu_medical_genetics": 5,
    "mmlu_miscellaneous": 5,
    "mmlu_moral_disputes": 5,
    "mmlu_moral_scenarios": 5,
    "mmlu_nutrition": 5,
    "mmlu_philosophy": 5,
    "mmlu_prehistory": 5,
    "mmlu_professional_accounting": 5,
    "mmlu_professional_law": 5,
    "mmlu_professional_medicine": 5,
    "mmlu_professional_psychology": 5,
    "mmlu_public_relations": 5,
    "mmlu_security_studies": 5,
    "mmlu_sociology": 5,
    "mmlu_us_foreign_policy": 5,
    "mmlu_virology": 5,
    "mmlu_world_religions": 5,
    "truthfulqa_gen": 0,
    "truthfulqa_mc1": 0,
    "truthfulqa_mc2": 0,
    "winogrande": 5
  },
  "higher_is_better": {
    "arc_challenge": {
      "acc": true,
      "acc_norm": true
    },
    "gsm8k": {
      "exact_match": true
    },
    "hellaswag": {
      "acc": true,
      "acc_norm": true
    },
    "mmlu": {
      "acc": true,
      "acc_norm": true,
      "bleu_max": true,
      "bleu_acc": true,
      "bleu_diff": true,
      "rouge1_max": true,
      "rouge1_acc": true,
      "rouge1_diff": true,
      "rouge2_max": true,
      "rouge2_acc": true,
      "rouge2_diff": true,
      "rougeL_max": true,
      "rougeL_acc": true,
      "rougeL_diff": true,
      "exact_match": true
    },
    "mmlu_abstract_algebra": {
      "acc": true
    },
    "mmlu_anatomy": {
      "acc": true
    },
    "mmlu_astronomy": {
      "acc": true
    },
    "mmlu_business_ethics": {
      "acc": true
    },
    "mmlu_clinical_knowledge": {
      "acc": true
    },
    "mmlu_college_biology": {
      "acc": true
    },
    "mmlu_college_chemistry": {
      "acc": true
    },
    "mmlu_college_computer_science": {
      "acc": true
    },
    "mmlu_college_mathematics": {
      "acc": true
    },
    "mmlu_college_medicine": {
      "acc": true
    },
    "mmlu_college_physics": {
      "acc": true
    },
    "mmlu_computer_security": {
      "acc": true
    },
    "mmlu_conceptual_physics": {
      "acc": true
    },
    "mmlu_econometrics": {
      "acc": true
    },
    "mmlu_electrical_engineering": {
      "acc": true
    },
    "mmlu_elementary_mathematics": {
      "acc": true
    },
    "mmlu_formal_logic": {
      "acc": true
    },
    "mmlu_global_facts": {
      "acc": true
    },
    "mmlu_high_school_biology": {
      "acc": true
    },
    "mmlu_high_school_chemistry": {
      "acc": true
    },
    "mmlu_high_school_computer_science": {
      "acc": true
    },
    "mmlu_high_school_european_history": {
      "acc": true
    },
    "mmlu_high_school_geography": {
      "acc": true
    },
    "mmlu_high_school_government_and_politics": {
      "acc": true
    },
    "mmlu_high_school_macroeconomics": {
      "acc": true
    },
    "mmlu_high_school_mathematics": {
      "acc": true
    },
    "mmlu_high_school_microeconomics": {
      "acc": true
    },
    "mmlu_high_school_physics": {
      "acc": true
    },
    "mmlu_high_school_psychology": {
      "acc": true
    },
    "mmlu_high_school_statistics": {
      "acc": true
    },
    "mmlu_high_school_us_history": {
      "acc": true
    },
    "mmlu_high_school_world_history": {
      "acc": true
    },
    "mmlu_human_aging": {
      "acc": true
    },
    "mmlu_human_sexuality": {
      "acc": true
    },
    "mmlu_humanities": {
      "acc": true,
      "acc_norm": true,
      "bleu_max": true,
      "bleu_acc": true,
      "bleu_diff": true,
      "rouge1_max": true,
      "rouge1_acc": true,
      "rouge1_diff": true,
      "rouge2_max": true,
      "rouge2_acc": true,
      "rouge2_diff": true,
      "rougeL_max": true,
      "rougeL_acc": true,
      "rougeL_diff": true,
      "exact_match": true
    },
    "mmlu_international_law": {
      "acc": true
    },
    "mmlu_jurisprudence": {
      "acc": true
    },
    "mmlu_logical_fallacies": {
      "acc": true
    },
    "mmlu_machine_learning": {
      "acc": true
    },
    "mmlu_management": {
      "acc": true
    },
    "mmlu_marketing": {
      "acc": true
    },
    "mmlu_medical_genetics": {
      "acc": true
    },
    "mmlu_miscellaneous": {
      "acc": true
    },
    "mmlu_moral_disputes": {
      "acc": true
    },
    "mmlu_moral_scenarios": {
      "acc": true
    },
    "mmlu_nutrition": {
      "acc": true
    },
    "mmlu_other": {
      "acc": true,
      "acc_norm": true,
      "bleu_max": true,
      "bleu_acc": true,
      "bleu_diff": true,
      "rouge1_max": true,
      "rouge1_acc": true,
      "rouge1_diff": true,
      "rouge2_max": true,
      "rouge2_acc": true,
      "rouge2_diff": true,
      "rougeL_max": true,
      "rougeL_acc": true,
      "rougeL_diff": true,
      "exact_match": true
    },
    "mmlu_philosophy": {
      "acc": true
    },
    "mmlu_prehistory": {
      "acc": true
    },
    "mmlu_professional_accounting": {
      "acc": true
    },
    "mmlu_professional_law": {
      "acc": true
    },
    "mmlu_professional_medicine": {
      "acc": true
    },
    "mmlu_professional_psychology": {
      "acc": true
    },
    "mmlu_public_relations": {
      "acc": true
    },
    "mmlu_security_studies": {
      "acc": true
    },
    "mmlu_social_sciences": {
      "acc": true,
      "acc_norm": true,
      "bleu_max": true,
      "bleu_acc": true,
      "bleu_diff": true,
      "rouge1_max": true,
      "rouge1_acc": true,
      "rouge1_diff": true,
      "rouge2_max": true,
      "rouge2_acc": true,
      "rouge2_diff": true,
      "rougeL_max": true,
      "rougeL_acc": true,
      "rougeL_diff": true,
      "exact_match": true
    },
    "mmlu_sociology": {
      "acc": true
    },
    "mmlu_stem": {
      "acc": true,
      "acc_norm": true,
      "bleu_max": true,
      "bleu_acc": true,
      "bleu_diff": true,
      "rouge1_max": true,
      "rouge1_acc": true,
      "rouge1_diff": true,
      "rouge2_max": true,
      "rouge2_acc": true,
      "rouge2_diff": true,
      "rougeL_max": true,
      "rougeL_acc": true,
      "rougeL_diff": true,
      "exact_match": true
    },
    "mmlu_us_foreign_policy": {
      "acc": true
    },
    "mmlu_virology": {
      "acc": true
    },
    "mmlu_world_religions": {
      "acc": true
    },
    "openllm": {
      "acc": true,
      "acc_norm": true,
      "bleu_max": true,
      "bleu_acc": true,
      "bleu_diff": true,
      "rouge1_max": true,
      "rouge1_acc": true,
      "rouge1_diff": true,
      "rouge2_max": true,
      "rouge2_acc": true,
      "rouge2_diff": true,
      "rougeL_max": true,
      "rougeL_acc": true,
      "rougeL_diff": true,
      "exact_match": true
    },
    "truthfulqa_gen": {
      "bleu_max": true,
      "bleu_acc": true,
      "bleu_diff": true,
      "rouge1_max": true,
      "rouge1_acc": true,
      "rouge1_diff": true,
      "rouge2_max": true,
      "rouge2_acc": true,
      "rouge2_diff": true,
      "rougeL_max": true,
      "rougeL_acc": true,
      "rougeL_diff": true
    },
    "truthfulqa_mc1": {
      "acc": true
    },
    "truthfulqa_mc2": {
      "acc": true
    },
    "winogrande": {
      "acc": true
    }
  },
  "n-samples": {
    "arc_challenge": {
      "original": 1172,
      "effective": 1172
    },
    "hellaswag": {
      "original": 10042,
      "effective": 10042
    },
    "truthfulqa_mc2": {
      "original": 817,
      "effective": 817
    },
    "truthfulqa_mc1": {
      "original": 817,
      "effective": 817
    },
    "truthfulqa_gen": {
      "original": 817,
      "effective": 817
    },
    "mmlu_anatomy": {
      "original": 135,
      "effective": 135
    },
    "mmlu_college_physics": {
      "original": 102,
      "effective": 102
    },
    "mmlu_computer_security": {
      "original": 100,
      "effective": 100
    },
    "mmlu_conceptual_physics": {
      "original": 235,
      "effective": 235
    },
    "mmlu_high_school_chemistry": {
      "original": 203,
      "effective": 203
    },
    "mmlu_college_computer_science": {
      "original": 100,
      "effective": 100
    },
    "mmlu_high_school_biology": {
      "original": 310,
      "effective": 310
    },
    "mmlu_high_school_mathematics": {
      "original": 270,
      "effective": 270
    },
    "mmlu_high_school_statistics": {
      "original": 216,
      "effective": 216
    },
    "mmlu_college_chemistry": {
      "original": 100,
      "effective": 100
    },
    "mmlu_electrical_engineering": {
      "original": 145,
      "effective": 145
    },
    "mmlu_college_mathematics": {
      "original": 100,
      "effective": 100
    },
    "mmlu_college_biology": {
      "original": 144,
      "effective": 144
    },
    "mmlu_abstract_algebra": {
      "original": 100,
      "effective": 100
    },
    "mmlu_elementary_mathematics": {
      "original": 378,
      "effective": 378
    },
    "mmlu_high_school_physics": {
      "original": 151,
      "effective": 151
    },
    "mmlu_high_school_computer_science": {
      "original": 100,
      "effective": 100
    },
    "mmlu_machine_learning": {
      "original": 112,
      "effective": 112
    },
    "mmlu_astronomy": {
      "original": 152,
      "effective": 152
    },
    "mmlu_professional_accounting": {
      "original": 282,
      "effective": 282
    },
    "mmlu_global_facts": {
      "original": 100,
      "effective": 100
    },
    "mmlu_college_medicine": {
      "original": 173,
      "effective": 173
    },
    "mmlu_virology": {
      "original": 166,
      "effective": 166
    },
    "mmlu_human_aging": {
      "original": 223,
      "effective": 223
    },
    "mmlu_professional_medicine": {
      "original": 272,
      "effective": 272
    },
    "mmlu_medical_genetics": {
      "original": 100,
      "effective": 100
    },
    "mmlu_business_ethics": {
      "original": 100,
      "effective": 100
    },
    "mmlu_management": {
      "original": 103,
      "effective": 103
    },
    "mmlu_miscellaneous": {
      "original": 783,
      "effective": 783
    },
    "mmlu_nutrition": {
      "original": 306,
      "effective": 306
    },
    "mmlu_marketing": {
      "original": 234,
      "effective": 234
    },
    "mmlu_clinical_knowledge": {
      "original": 265,
      "effective": 265
    },
    "mmlu_high_school_psychology": {
      "original": 545,
      "effective": 545
    },
    "mmlu_us_foreign_policy": {
      "original": 100,
      "effective": 100
    },
    "mmlu_professional_psychology": {
      "original": 612,
      "effective": 612
    },
    "mmlu_human_sexuality": {
      "original": 131,
      "effective": 131
    },
    "mmlu_public_relations": {
      "original": 110,
      "effective": 110
    },
    "mmlu_security_studies": {
      "original": 245,
      "effective": 245
    },
    "mmlu_sociology": {
      "original": 201,
      "effective": 201
    },
    "mmlu_high_school_microeconomics": {
      "original": 238,
      "effective": 238
    },
    "mmlu_high_school_macroeconomics": {
      "original": 390,
      "effective": 390
    },
    "mmlu_high_school_government_and_politics": {
      "original": 193,
      "effective": 193
    },
    "mmlu_econometrics": {
      "original": 114,
      "effective": 114
    },
    "mmlu_high_school_geography": {
      "original": 198,
      "effective": 198
    },
    "mmlu_prehistory": {
      "original": 324,
      "effective": 324
    },
    "mmlu_moral_scenarios": {
      "original": 895,
      "effective": 895
    },
    "mmlu_moral_disputes": {
      "original": 346,
      "effective": 346
    },
    "mmlu_philosophy": {
      "original": 311,
      "effective": 311
    },
    "mmlu_high_school_world_history": {
      "original": 237,
      "effective": 237
    },
    "mmlu_logical_fallacies": {
      "original": 163,
      "effective": 163
    },
    "mmlu_formal_logic": {
      "original": 126,
      "effective": 126
    },
    "mmlu_high_school_us_history": {
      "original": 204,
      "effective": 204
    },
    "mmlu_high_school_european_history": {
      "original": 165,
      "effective": 165
    },
    "mmlu_professional_law": {
      "original": 1534,
      "effective": 1534
    },
    "mmlu_jurisprudence": {
      "original": 108,
      "effective": 108
    },
    "mmlu_international_law": {
      "original": 121,
      "effective": 121
    },
    "mmlu_world_religions": {
      "original": 171,
      "effective": 171
    },
    "winogrande": {
      "original": 1267,
      "effective": 1267
    },
    "gsm8k": {
      "original": 1319,
      "effective": 1319
    }
  },
  "config": {
    "model": "sparseml",
    "model_args": "pretrained=Qwen__Qwen2-1.5B-Instruct,dtype=auto,add_bos_token=True,max_seq_len=4096",
    "model_num_parameters": 1545004544,
    "model_dtype": "torch.bfloat16",
    "model_revision": "main",
    "model_sha": "",
    "batch_size": "auto",
    "batch_sizes": [
      8
    ],
    "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": null,
  "date": 1720650836.9117608,
  "pretty_env_info": "PyTorch version: 2.3.1+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 3.26.3\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Mar 22 2024, 16:50:05) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-5.15.0-91-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.3.103\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: 545.23.08\ncuDNN version: Could not collect\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):                             256\nOn-line CPU(s) list:                0-255\nVendor ID:                          AuthenticAMD\nModel name:                         AMD EPYC 7763 64-Core Processor\nCPU family:                         25\nModel:                              1\nThread(s) per core:                 2\nCore(s) per socket:                 64\nSocket(s):                          2\nStepping:                           1\nFrequency boost:                    enabled\nCPU max MHz:                        3529.0520\nCPU min MHz:                        1500.0000\nBogoMIPS:                           4900.16\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 nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm\nVirtualization:                     AMD-V\nL1d cache:                          4 MiB (128 instances)\nL1i cache:                          4 MiB (128 instances)\nL2 cache:                           64 MiB (128 instances)\nL3 cache:                           512 MiB (16 instances)\nNUMA node(s):                       2\nNUMA node0 CPU(s):                  0-63,128-191\nNUMA node1 CPU(s):                  64-127,192-255\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:             Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET\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, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS 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==1.14.1\n[pip3] onnxruntime==1.18.0\n[pip3] torch==2.3.1\n[pip3] triton==2.3.1\n[conda] Could not collect",
  "transformers_version": "4.42.3",
  "upper_git_hash": null,
  "tokenizer_pad_token": [
    "<|endoftext|>",
    "151643"
  ],
  "tokenizer_eos_token": [
    "<|im_end|>",
    "151645"
  ],
  "tokenizer_bos_token": [
    null,
    "None"
  ],
  "eot_token_id": 151645,
  "max_length": 32768,
  "task_hashes": {},
  "model_source": "sparseml",
  "model_name": "Qwen__Qwen2-1.5B-Instruct",
  "model_name_sanitized": "Qwen__Qwen2-1.5B-Instruct",
  "system_instruction": null,
  "system_instruction_sha": null,
  "fewshot_as_multiturn": false,
  "chat_template": null,
  "chat_template_sha": null,
  "start_time": 14715912.198156057,
  "end_time": 14752747.761713237,
  "total_evaluation_time_seconds": "36835.563557179645"
}