evaluations/en/Mistral-7B-Instruct-v0.3/minerva_math_4_shot.json

{
  "results": {
    "minerva_math": {
      "exact_match,none": 0.1344,
      "exact_match_stderr,none": 0.00469690840313393,
      "alias": "minerva_math"
    },
    "minerva_math_algebra": {
      "alias": " - minerva_math_algebra",
      "exact_match,none": 0.1954507160909857,
      "exact_match_stderr,none": 0.011514699662714494
    },
    "minerva_math_counting_and_prob": {
      "alias": " - minerva_math_counting_and_prob",
      "exact_match,none": 0.12236286919831224,
      "exact_match_stderr,none": 0.015067866025208529
    },
    "minerva_math_geometry": {
      "alias": " - minerva_math_geometry",
      "exact_match,none": 0.09603340292275574,
      "exact_match_stderr,none": 0.013476384772608527
    },
    "minerva_math_intermediate_algebra": {
      "alias": " - minerva_math_intermediate_algebra",
      "exact_match,none": 0.04540420819490587,
      "exact_match_stderr,none": 0.006931935965006335
    },
    "minerva_math_num_theory": {
      "alias": " - minerva_math_num_theory",
      "exact_match,none": 0.08148148148148149,
      "exact_match_stderr,none": 0.011783628281121686
    },
    "minerva_math_prealgebra": {
      "alias": " - minerva_math_prealgebra",
      "exact_match,none": 0.2571756601607348,
      "exact_match_stderr,none": 0.014818299496867965
    },
    "minerva_math_precalc": {
      "alias": " - minerva_math_precalc",
      "exact_match,none": 0.04945054945054945,
      "exact_match_stderr,none": 0.009286983354895582
    }
  },
  "groups": {
    "minerva_math": {
      "exact_match,none": 0.1344,
      "exact_match_stderr,none": 0.00469690840313393,
      "alias": "minerva_math"
    }
  },
  "group_subtasks": {
    "minerva_math": [
      "minerva_math_algebra",
      "minerva_math_counting_and_prob",
      "minerva_math_geometry",
      "minerva_math_intermediate_algebra",
      "minerva_math_num_theory",
      "minerva_math_prealgebra",
      "minerva_math_precalc"
    ]
  },
  "configs": {
    "minerva_math_algebra": {
      "task": "minerva_math_algebra",
      "tag": [
        "math_word_problems"
      ],
      "group": [
        "math_word_problems"
      ],
      "dataset_path": "EleutherAI/hendrycks_math",
      "dataset_name": "algebra",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "training_split": "train",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_doc(doc: dict) -> dict:\n        out_doc = {\n            \"problem\": doc[\"problem\"],\n            \"solution\": doc[\"solution\"],\n            \"answer\": normalize_final_answer(\n                remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n            ),\n        }\n        if getattr(doc, \"few_shot\", None) is not None:\n            out_doc[\"few_shot\"] = True\n        return out_doc\n\n    return dataset.map(_process_doc)\n",
      "doc_to_text": "def doc_to_text(doc: dict) -> str:\n    return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
      "doc_to_target": "{{answer if few_shot is undefined else solution}}",
      "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n    candidates = results[0]\n\n    unnormalized_answer = get_unnormalized_answer(candidates)\n    answer = normalize_final_answer(unnormalized_answer)\n\n    if is_equiv(answer, doc[\"answer\"]):\n        retval = 1\n    else:\n        retval = 0\n\n    results = {\n        \"exact_match\": retval,\n    }\n    return results\n",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n",
        "samples": "<function list_fewshot_samples at 0x14c88c892290>"
      },
      "num_fewshot": 4,
      "metric_list": [
        {
          "metric": "exact_match",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "generate_until",
      "generation_kwargs": {
        "until": [
          "Problem:"
        ],
        "do_sample": false,
        "temperature": 0.0
      },
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 1.0
      }
    },
    "minerva_math_counting_and_prob": {
      "task": "minerva_math_counting_and_prob",
      "tag": [
        "math_word_problems"
      ],
      "group": [
        "math_word_problems"
      ],
      "dataset_path": "EleutherAI/hendrycks_math",
      "dataset_name": "counting_and_probability",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "training_split": "train",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_doc(doc: dict) -> dict:\n        out_doc = {\n            \"problem\": doc[\"problem\"],\n            \"solution\": doc[\"solution\"],\n            \"answer\": normalize_final_answer(\n                remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n            ),\n        }\n        if getattr(doc, \"few_shot\", None) is not None:\n            out_doc[\"few_shot\"] = True\n        return out_doc\n\n    return dataset.map(_process_doc)\n",
      "doc_to_text": "def doc_to_text(doc: dict) -> str:\n    return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
      "doc_to_target": "{{answer if few_shot is undefined else solution}}",
      "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n    candidates = results[0]\n\n    unnormalized_answer = get_unnormalized_answer(candidates)\n    answer = normalize_final_answer(unnormalized_answer)\n\n    if is_equiv(answer, doc[\"answer\"]):\n        retval = 1\n    else:\n        retval = 0\n\n    results = {\n        \"exact_match\": retval,\n    }\n    return results\n",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n",
        "samples": "<function list_fewshot_samples at 0x14c88c890310>"
      },
      "num_fewshot": 4,
      "metric_list": [
        {
          "metric": "exact_match",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "generate_until",
      "generation_kwargs": {
        "until": [
          "Problem:"
        ],
        "do_sample": false,
        "temperature": 0.0
      },
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 1.0
      }
    },
    "minerva_math_geometry": {
      "task": "minerva_math_geometry",
      "tag": [
        "math_word_problems"
      ],
      "group": [
        "math_word_problems"
      ],
      "dataset_path": "EleutherAI/hendrycks_math",
      "dataset_name": "geometry",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "training_split": "train",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_doc(doc: dict) -> dict:\n        out_doc = {\n            \"problem\": doc[\"problem\"],\n            \"solution\": doc[\"solution\"],\n            \"answer\": normalize_final_answer(\n                remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n            ),\n        }\n        if getattr(doc, \"few_shot\", None) is not None:\n            out_doc[\"few_shot\"] = True\n        return out_doc\n\n    return dataset.map(_process_doc)\n",
      "doc_to_text": "def doc_to_text(doc: dict) -> str:\n    return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
      "doc_to_target": "{{answer if few_shot is undefined else solution}}",
      "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n    candidates = results[0]\n\n    unnormalized_answer = get_unnormalized_answer(candidates)\n    answer = normalize_final_answer(unnormalized_answer)\n\n    if is_equiv(answer, doc[\"answer\"]):\n        retval = 1\n    else:\n        retval = 0\n\n    results = {\n        \"exact_match\": retval,\n    }\n    return results\n",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n",
        "samples": "<function list_fewshot_samples at 0x14c88c86d000>"
      },
      "num_fewshot": 4,
      "metric_list": [
        {
          "metric": "exact_match",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "generate_until",
      "generation_kwargs": {
        "until": [
          "Problem:"
        ],
        "do_sample": false,
        "temperature": 0.0
      },
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 1.0
      }
    },
    "minerva_math_intermediate_algebra": {
      "task": "minerva_math_intermediate_algebra",
      "tag": [
        "math_word_problems"
      ],
      "group": [
        "math_word_problems"
      ],
      "dataset_path": "EleutherAI/hendrycks_math",
      "dataset_name": "intermediate_algebra",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "training_split": "train",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_doc(doc: dict) -> dict:\n        out_doc = {\n            \"problem\": doc[\"problem\"],\n            \"solution\": doc[\"solution\"],\n            \"answer\": normalize_final_answer(\n                remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n            ),\n        }\n        if getattr(doc, \"few_shot\", None) is not None:\n            out_doc[\"few_shot\"] = True\n        return out_doc\n\n    return dataset.map(_process_doc)\n",
      "doc_to_text": "def doc_to_text(doc: dict) -> str:\n    return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
      "doc_to_target": "{{answer if few_shot is undefined else solution}}",
      "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n    candidates = results[0]\n\n    unnormalized_answer = get_unnormalized_answer(candidates)\n    answer = normalize_final_answer(unnormalized_answer)\n\n    if is_equiv(answer, doc[\"answer\"]):\n        retval = 1\n    else:\n        retval = 0\n\n    results = {\n        \"exact_match\": retval,\n    }\n    return results\n",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n",
        "samples": "<function list_fewshot_samples at 0x14c88c813f40>"
      },
      "num_fewshot": 4,
      "metric_list": [
        {
          "metric": "exact_match",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "generate_until",
      "generation_kwargs": {
        "until": [
          "Problem:"
        ],
        "do_sample": false,
        "temperature": 0.0
      },
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 1.0
      }
    },
    "minerva_math_num_theory": {
      "task": "minerva_math_num_theory",
      "tag": [
        "math_word_problems"
      ],
      "group": [
        "math_word_problems"
      ],
      "dataset_path": "EleutherAI/hendrycks_math",
      "dataset_name": "number_theory",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "training_split": "train",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_doc(doc: dict) -> dict:\n        out_doc = {\n            \"problem\": doc[\"problem\"],\n            \"solution\": doc[\"solution\"],\n            \"answer\": normalize_final_answer(\n                remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n            ),\n        }\n        if getattr(doc, \"few_shot\", None) is not None:\n            out_doc[\"few_shot\"] = True\n        return out_doc\n\n    return dataset.map(_process_doc)\n",
      "doc_to_text": "def doc_to_text(doc: dict) -> str:\n    return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
      "doc_to_target": "{{answer if few_shot is undefined else solution}}",
      "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n    candidates = results[0]\n\n    unnormalized_answer = get_unnormalized_answer(candidates)\n    answer = normalize_final_answer(unnormalized_answer)\n\n    if is_equiv(answer, doc[\"answer\"]):\n        retval = 1\n    else:\n        retval = 0\n\n    results = {\n        \"exact_match\": retval,\n    }\n    return results\n",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n",
        "samples": "<function list_fewshot_samples at 0x14c88c8104c0>"
      },
      "num_fewshot": 4,
      "metric_list": [
        {
          "metric": "exact_match",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "generate_until",
      "generation_kwargs": {
        "until": [
          "Problem:"
        ],
        "do_sample": false,
        "temperature": 0.0
      },
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 1.0
      }
    },
    "minerva_math_prealgebra": {
      "task": "minerva_math_prealgebra",
      "tag": [
        "math_word_problems"
      ],
      "group": [
        "math_word_problems"
      ],
      "dataset_path": "EleutherAI/hendrycks_math",
      "dataset_name": "prealgebra",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "training_split": "train",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_doc(doc: dict) -> dict:\n        out_doc = {\n            \"problem\": doc[\"problem\"],\n            \"solution\": doc[\"solution\"],\n            \"answer\": normalize_final_answer(\n                remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n            ),\n        }\n        if getattr(doc, \"few_shot\", None) is not None:\n            out_doc[\"few_shot\"] = True\n        return out_doc\n\n    return dataset.map(_process_doc)\n",
      "doc_to_text": "def doc_to_text(doc: dict) -> str:\n    return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
      "doc_to_target": "{{answer if few_shot is undefined else solution}}",
      "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n    candidates = results[0]\n\n    unnormalized_answer = get_unnormalized_answer(candidates)\n    answer = normalize_final_answer(unnormalized_answer)\n\n    if is_equiv(answer, doc[\"answer\"]):\n        retval = 1\n    else:\n        retval = 0\n\n    results = {\n        \"exact_match\": retval,\n    }\n    return results\n",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n",
        "samples": "<function list_fewshot_samples at 0x14c9228fbac0>"
      },
      "num_fewshot": 4,
      "metric_list": [
        {
          "metric": "exact_match",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "generate_until",
      "generation_kwargs": {
        "until": [
          "Problem:"
        ],
        "do_sample": false,
        "temperature": 0.0
      },
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 1.0
      }
    },
    "minerva_math_precalc": {
      "task": "minerva_math_precalc",
      "tag": [
        "math_word_problems"
      ],
      "group": [
        "math_word_problems"
      ],
      "dataset_path": "EleutherAI/hendrycks_math",
      "dataset_name": "precalculus",
      "dataset_kwargs": {
        "trust_remote_code": true
      },
      "training_split": "train",
      "test_split": "test",
      "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n    def _process_doc(doc: dict) -> dict:\n        out_doc = {\n            \"problem\": doc[\"problem\"],\n            \"solution\": doc[\"solution\"],\n            \"answer\": normalize_final_answer(\n                remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n            ),\n        }\n        if getattr(doc, \"few_shot\", None) is not None:\n            out_doc[\"few_shot\"] = True\n        return out_doc\n\n    return dataset.map(_process_doc)\n",
      "doc_to_text": "def doc_to_text(doc: dict) -> str:\n    return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
      "doc_to_target": "{{answer if few_shot is undefined else solution}}",
      "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n    candidates = results[0]\n\n    unnormalized_answer = get_unnormalized_answer(candidates)\n    answer = normalize_final_answer(unnormalized_answer)\n\n    if is_equiv(answer, doc[\"answer\"]):\n        retval = 1\n    else:\n        retval = 0\n\n    results = {\n        \"exact_match\": retval,\n    }\n    return results\n",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "fewshot_config": {
        "sampler": "first_n",
        "samples": "<function list_fewshot_samples at 0x14c8ad211090>"
      },
      "num_fewshot": 4,
      "metric_list": [
        {
          "metric": "exact_match",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "generate_until",
      "generation_kwargs": {
        "until": [
          "Problem:"
        ],
        "do_sample": false,
        "temperature": 0.0
      },
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 1.0
      }
    }
  },
  "versions": {
    "minerva_math": 1.0,
    "minerva_math_algebra": 1.0,
    "minerva_math_counting_and_prob": 1.0,
    "minerva_math_geometry": 1.0,
    "minerva_math_intermediate_algebra": 1.0,
    "minerva_math_num_theory": 1.0,
    "minerva_math_prealgebra": 1.0,
    "minerva_math_precalc": 1.0
  },
  "n-shot": {
    "minerva_math_algebra": 4,
    "minerva_math_counting_and_prob": 4,
    "minerva_math_geometry": 4,
    "minerva_math_intermediate_algebra": 4,
    "minerva_math_num_theory": 4,
    "minerva_math_prealgebra": 4,
    "minerva_math_precalc": 4
  },
  "higher_is_better": {
    "minerva_math": {
      "exact_match": true
    },
    "minerva_math_algebra": {
      "exact_match": true
    },
    "minerva_math_counting_and_prob": {
      "exact_match": true
    },
    "minerva_math_geometry": {
      "exact_match": true
    },
    "minerva_math_intermediate_algebra": {
      "exact_match": true
    },
    "minerva_math_num_theory": {
      "exact_match": true
    },
    "minerva_math_prealgebra": {
      "exact_match": true
    },
    "minerva_math_precalc": {
      "exact_match": true
    }
  },
  "n-samples": {
    "minerva_math_algebra": {
      "original": 1187,
      "effective": 1187
    },
    "minerva_math_counting_and_prob": {
      "original": 474,
      "effective": 474
    },
    "minerva_math_geometry": {
      "original": 479,
      "effective": 479
    },
    "minerva_math_intermediate_algebra": {
      "original": 903,
      "effective": 903
    },
    "minerva_math_num_theory": {
      "original": 540,
      "effective": 540
    },
    "minerva_math_prealgebra": {
      "original": 871,
      "effective": 871
    },
    "minerva_math_precalc": {
      "original": 546,
      "effective": 546
    }
  },
  "config": {
    "model": "hf",
    "model_args": "parallelize=True,pretrained=mistralai/Mistral-7B-Instruct-v0.3,trust_remote_code=True,mm=False,trust_remote_code=True",
    "model_num_parameters": 7248023552,
    "model_dtype": "torch.bfloat16",
    "model_revision": "main",
    "model_sha": "e0bc86c23ce5aae1db576c8cca6f06f1f73af2db",
    "batch_size": 1,
    "batch_sizes": [],
    "device": null,
    "use_cache": null,
    "limit": null,
    "bootstrap_iters": 100000,
    "gen_kwargs": null,
    "random_seed": 0,
    "numpy_seed": 1234,
    "torch_seed": 1234,
    "fewshot_seed": 1234
  },
  "git_hash": "3127d82f",
  "date": 1732457421.434201,
  "pretty_env_info": "PyTorch version: 2.1.0a0+29c30b1\nIs debug build: False\nCUDA used to build PyTorch: 12.2\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.27.1\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-5.15.0-1064-azure-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.2.128\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\n\nNvidia driver version: 535.161.08\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.4\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture:                       x86_64\nCPU op-mode(s):                     32-bit, 64-bit\nAddress sizes:                      48 bits physical, 48 bits virtual\nByte Order:                         Little Endian\nCPU(s):                             96\nOn-line CPU(s) list:                0-95\nVendor ID:                          AuthenticAMD\nModel name:                         AMD EPYC 7V12 64-Core Processor\nCPU family:                         23\nModel:                              49\nThread(s) per core:                 1\nCore(s) per socket:                 48\nSocket(s):                          2\nStepping:                           0\nBogoMIPS:                           4890.89\nFlags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru arat umip rdpid\nHypervisor vendor:                  Microsoft\nVirtualization type:                full\nL1d cache:                          3 MiB (96 instances)\nL1i cache:                          3 MiB (96 instances)\nL2 cache:                           48 MiB (96 instances)\nL3 cache:                           384 MiB (24 instances)\nNUMA node(s):                       4\nNUMA node0 CPU(s):                  0-23\nNUMA node1 CPU(s):                  24-47\nNUMA node2 CPU(s):                  48-71\nNUMA node3 CPU(s):                  72-95\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit:        Not affected\nVulnerability L1tf:                 Not affected\nVulnerability Mds:                  Not affected\nVulnerability Meltdown:             Not affected\nVulnerability Mmio stale data:      Not affected\nVulnerability Retbleed:             Mitigation; untrained return thunk; SMT disabled\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2:           Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds:                Not affected\nVulnerability Tsx async abort:      Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.22.2\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.1.0a0+29c30b1\n[pip3] torch-tensorrt==2.0.0.dev0\n[pip3] torchaudio==2.1.0\n[pip3] torchdata==0.7.0a0\n[pip3] torchmetrics==1.2.0\n[pip3] torchvision==0.16.0a0\n[pip3] triton==2.0.0.dev20221202\n[conda] Could not collect",
  "transformers_version": "4.46.3",
  "upper_git_hash": null,
  "tokenizer_pad_token": [
    "<unk>",
    "0"
  ],
  "tokenizer_eos_token": [
    "</s>",
    "2"
  ],
  "tokenizer_bos_token": [
    "<s>",
    "1"
  ],
  "eot_token_id": 2,
  "max_length": 32768,
  "task_hashes": {},
  "model_source": "hf",
  "model_name": "mistralai/Mistral-7B-Instruct-v0.3",
  "model_name_sanitized": "mistralai__Mistral-7B-Instruct-v0.3",
  "system_instruction": null,
  "system_instruction_sha": null,
  "fewshot_as_multiturn": false,
  "chat_template": null,
  "chat_template_sha": null,
  "start_time": 937481.096308053,
  "end_time": 984028.729417881,
  "total_evaluation_time_seconds": "46547.63310982799"
}