{
"results": {
"agieval": {
"acc,none": 0.303096274794388,
"acc_stderr,none": 0.00482515607580441,
"alias": "agieval"
},
"agieval_aqua_rat": {
"alias": " - agieval_aqua_rat",
"acc,none": 0.20078740157480315,
"acc_stderr,none": 0.025184836154107815,
"acc_norm,none": 0.20866141732283464,
"acc_norm_stderr,none": 0.02554712225493389
},
"agieval_gaokao_biology": {
"alias": " - agieval_gaokao_biology",
"acc,none": 0.23809523809523808,
"acc_stderr,none": 0.02946134404236891,
"acc_norm,none": 0.2571428571428571,
"acc_norm_stderr,none": 0.030231990420749873
},
"agieval_gaokao_chemistry": {
"alias": " - agieval_gaokao_chemistry",
"acc,none": 0.23671497584541062,
"acc_stderr,none": 0.029615742669460064,
"acc_norm,none": 0.2560386473429952,
"acc_norm_stderr,none": 0.030408453922393275
},
"agieval_gaokao_chinese": {
"alias": " - agieval_gaokao_chinese",
"acc,none": 0.2032520325203252,
"acc_stderr,none": 0.025709574472913603,
"acc_norm,none": 0.21544715447154472,
"acc_norm_stderr,none": 0.02626627216557685
},
"agieval_gaokao_english": {
"alias": " - agieval_gaokao_english",
"acc,none": 0.5947712418300654,
"acc_stderr,none": 0.028110928492809068,
"acc_norm,none": 0.5620915032679739,
"acc_norm_stderr,none": 0.02840830202033269
},
"agieval_gaokao_geography": {
"alias": " - agieval_gaokao_geography",
"acc,none": 0.23115577889447236,
"acc_stderr,none": 0.029959803439140426,
"acc_norm,none": 0.24623115577889448,
"acc_norm_stderr,none": 0.030616673158037285
},
"agieval_gaokao_history": {
"alias": " - agieval_gaokao_history",
"acc,none": 0.2936170212765957,
"acc_stderr,none": 0.029771642712491234,
"acc_norm,none": 0.2851063829787234,
"acc_norm_stderr,none": 0.02951319662553935
},
"agieval_gaokao_mathcloze": {
"alias": " - agieval_gaokao_mathcloze",
"acc,none": 0.025423728813559324,
"acc_stderr,none": 0.01455239952216709
},
"agieval_gaokao_mathqa": {
"alias": " - agieval_gaokao_mathqa",
"acc,none": 0.24786324786324787,
"acc_stderr,none": 0.023079184079532432,
"acc_norm,none": 0.28205128205128205,
"acc_norm_stderr,none": 0.024053414152940683
},
"agieval_gaokao_physics": {
"alias": " - agieval_gaokao_physics",
"acc,none": 0.27,
"acc_stderr,none": 0.03147145152843339,
"acc_norm,none": 0.28,
"acc_norm_stderr,none": 0.03182868716477581
},
"agieval_jec_qa_ca": {
"alias": " - agieval_jec_qa_ca",
"acc,none": 0.45345345345345345,
"acc_stderr,none": 0.015758492287110338,
"acc_norm,none": 0.4574574574574575,
"acc_norm_stderr,none": 0.015769829012649176
},
"agieval_jec_qa_kd": {
"alias": " - agieval_jec_qa_kd",
"acc,none": 0.5,
"acc_stderr,none": 0.015819299929208316,
"acc_norm,none": 0.484,
"acc_norm_stderr,none": 0.015811198373114878
},
"agieval_logiqa_en": {
"alias": " - agieval_logiqa_en",
"acc,none": 0.24423963133640553,
"acc_stderr,none": 0.016851689430077556,
"acc_norm,none": 0.2995391705069124,
"acc_norm_stderr,none": 0.017966441188587947
},
"agieval_logiqa_zh": {
"alias": " - agieval_logiqa_zh",
"acc,none": 0.2488479262672811,
"acc_stderr,none": 0.01695798590452558,
"acc_norm,none": 0.2887864823348694,
"acc_norm_stderr,none": 0.017775906336539235
},
"agieval_lsat_ar": {
"alias": " - agieval_lsat_ar",
"acc,none": 0.21739130434782608,
"acc_stderr,none": 0.02725685083881996,
"acc_norm,none": 0.20434782608695654,
"acc_norm_stderr,none": 0.02664580815001134
},
"agieval_lsat_lr": {
"alias": " - agieval_lsat_lr",
"acc,none": 0.28431372549019607,
"acc_stderr,none": 0.019994077265863584,
"acc_norm,none": 0.26862745098039215,
"acc_norm_stderr,none": 0.01964651988859971
},
"agieval_lsat_rc": {
"alias": " - agieval_lsat_rc",
"acc,none": 0.30111524163568776,
"acc_stderr,none": 0.028022169587612195,
"acc_norm,none": 0.2899628252788104,
"acc_norm_stderr,none": 0.0277168778552269
},
"agieval_math": {
"alias": " - agieval_math",
"acc,none": 0.103,
"acc_stderr,none": 0.009616833339695806
},
"agieval_sat_en": {
"alias": " - agieval_sat_en",
"acc,none": 0.48058252427184467,
"acc_stderr,none": 0.034895171350660135,
"acc_norm,none": 0.4563106796116505,
"acc_norm_stderr,none": 0.034787945997877434
},
"agieval_sat_en_without_passage": {
"alias": " - agieval_sat_en_without_passage",
"acc,none": 0.2621359223300971,
"acc_stderr,none": 0.03071669765614076,
"acc_norm,none": 0.24271844660194175,
"acc_norm_stderr,none": 0.029943540553570545
},
"agieval_sat_math": {
"alias": " - agieval_sat_math",
"acc,none": 0.2681818181818182,
"acc_stderr,none": 0.029936030014892836,
"acc_norm,none": 0.22272727272727272,
"acc_norm_stderr,none": 0.028115859018702657
}
},
"groups": {
"agieval": {
"acc,none": 0.303096274794388,
"acc_stderr,none": 0.00482515607580441,
"alias": "agieval"
}
},
"group_subtasks": {
"agieval": [
"agieval_gaokao_biology",
"agieval_gaokao_chemistry",
"agieval_gaokao_chinese",
"agieval_gaokao_geography",
"agieval_gaokao_history",
"agieval_gaokao_mathcloze",
"agieval_gaokao_mathqa",
"agieval_gaokao_physics",
"agieval_jec_qa_ca",
"agieval_jec_qa_kd",
"agieval_logiqa_zh",
"agieval_aqua_rat",
"agieval_gaokao_english",
"agieval_logiqa_en",
"agieval_lsat_ar",
"agieval_lsat_lr",
"agieval_lsat_rc",
"agieval_math",
"agieval_sat_en_without_passage",
"agieval_sat_en",
"agieval_sat_math"
]
},
"configs": {
"agieval_aqua_rat": {
"task": "agieval_aqua_rat",
"dataset_path": "hails/agieval-aqua-rat",
"test_split": "test",
"doc_to_text": "{{query}}",
"doc_to_target": "{{gold}}",
"doc_to_choice": "{{choices}}",
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"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
}
},
"agieval_gaokao_biology": {
"task": "agieval_gaokao_biology",
"dataset_path": "hails/agieval-gaokao-biology",
"test_split": "test",
"doc_to_text": "{{query}}",
"doc_to_target": "{{gold}}",
"doc_to_choice": "{{choices}}",
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"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
}
},
"agieval_gaokao_chemistry": {
"task": "agieval_gaokao_chemistry",
"dataset_path": "hails/agieval-gaokao-chemistry",
"test_split": "test",
"doc_to_text": "{{query}}",
"doc_to_target": "{{gold}}",
"doc_to_choice": "{{choices}}",
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"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
}
},
"agieval_gaokao_chinese": {
"task": "agieval_gaokao_chinese",
"dataset_path": "hails/agieval-gaokao-chinese",
"test_split": "test",
"doc_to_text": "{{query}}",
"doc_to_target": "{{gold}}",
"doc_to_choice": "{{choices}}",
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"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
}
},
"agieval_gaokao_english": {
"task": "agieval_gaokao_english",
"dataset_path": "hails/agieval-gaokao-english",
"test_split": "test",
"doc_to_text": "{{query}}",
"doc_to_target": "{{gold}}",
"doc_to_choice": "{{choices}}",
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"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
}
},
"agieval_gaokao_geography": {
"task": "agieval_gaokao_geography",
"dataset_path": "hails/agieval-gaokao-geography",
"test_split": "test",
"doc_to_text": "{{query}}",
"doc_to_target": "{{gold}}",
"doc_to_choice": "{{choices}}",
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"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
}
},
"agieval_gaokao_history": {
"task": "agieval_gaokao_history",
"dataset_path": "hails/agieval-gaokao-history",
"test_split": "test",
"doc_to_text": "{{query}}",
"doc_to_target": "{{gold}}",
"doc_to_choice": "{{choices}}",
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"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
}
},
"agieval_gaokao_mathcloze": {
"task": "agieval_gaokao_mathcloze",
"dataset_path": "hails/agieval-gaokao-mathcloze",
"test_split": "test",
"doc_to_text": "{{query}}",
"doc_to_target": "{{answer}}",
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidate = results[0]\n\n gold = doc[\"answer\"]\n\n if not gold:\n print(doc, candidate, gold)\n if is_equiv(candidate, gold):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"acc\": retval,\n }\n return results\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"max_gen_toks": 32,
"do_sample": false,
"temperature": 0.0,
"until": [
"Q:"
]
},
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"agieval_gaokao_mathqa": {
"task": "agieval_gaokao_mathqa",
"dataset_path": "hails/agieval-gaokao-mathqa",
"test_split": "test",
"doc_to_text": "{{query}}",
"doc_to_target": "{{gold}}",
"doc_to_choice": "{{choices}}",
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"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
}
},
"agieval_gaokao_physics": {
"task": "agieval_gaokao_physics",
"dataset_path": "hails/agieval-gaokao-physics",
"test_split": "test",
"doc_to_text": "{{query}}",
"doc_to_target": "{{gold}}",
"doc_to_choice": "{{choices}}",
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"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
}
},
"agieval_jec_qa_ca": {
"task": "agieval_jec_qa_ca",
"dataset_path": "hails/agieval-jec-qa-ca",
"test_split": "test",
"doc_to_text": "{{query}}",
"doc_to_target": "{{gold}}",
"doc_to_choice": "{{choices}}",
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"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
}
},
"agieval_jec_qa_kd": {
"task": "agieval_jec_qa_kd",
"dataset_path": "hails/agieval-jec-qa-kd",
"test_split": "test",
"doc_to_text": "{{query}}",
"doc_to_target": "{{gold}}",
"doc_to_choice": "{{choices}}",
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"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
}
},
"agieval_logiqa_en": {
"task": "agieval_logiqa_en",
"dataset_path": "hails/agieval-logiqa-en",
"test_split": "test",
"doc_to_text": "{{query}}",
"doc_to_target": "{{gold}}",
"doc_to_choice": "{{choices}}",
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"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
}
},
"agieval_logiqa_zh": {
"task": "agieval_logiqa_zh",
"dataset_path": "hails/agieval-logiqa-zh",
"test_split": "test",
"doc_to_text": "{{query}}",
"doc_to_target": "{{gold}}",
"doc_to_choice": "{{choices}}",
"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
"description": "",
"target_delimiter": " ",
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},
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"process_results": "def process_results_mcqa(doc, results):\n results = [result[0] for result in results]\n\n gold = doc[\"gold\"]\n\n acc = 1.0 if int(np.argmax(results)) in gold else 0.0\n completion_len = np.array([float(len(i)) for i in doc[\"choices\"]])\n acc_norm = 1.0 if int(np.argmax(results / completion_len)) in gold else 0.0\n\n return {\n \"acc\": acc,\n \"acc_norm\": acc_norm,\n }\n",
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},
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidate = results[0]\n\n gold = doc[\"answer\"]\n\n if not gold:\n print(doc, candidate, gold)\n if is_equiv(candidate, gold):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"acc\": retval,\n }\n return results\n",
"description": "",
"target_delimiter": " ",
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"description": "",
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"metadata": {
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}
},
"agieval_sat_en_without_passage": {
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"test_split": "test",
"doc_to_text": "{{query}}",
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"metadata": {
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}
},
"versions": {
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},
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},
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},
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},
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},
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},
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},
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},
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},
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},
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},
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},
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},
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},
"config": {
"model": "vllm",
"model_args": "pretrained=inceptionai/jais-family-13b-chat,tensor_parallel_size=1,data_parallel_size=2,gpu_memory_utilization=0.9,download_dir=/tmp,enforce_eager=True",
"batch_size": 1,
"batch_sizes": [],
"device": null,
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"limit": null,
"bootstrap_iters": 100000,
"gen_kwargs": null,
"random_seed": 0,
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},
"git_hash": "788a3672",
"date": 1737531942.5649998,
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"model_name_sanitized": "inceptionai__jais-family-13b-chat",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": null,
"chat_template_sha": null,
"start_time": 9754.916785406,
"end_time": 13055.959963057,
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}