533 lines
26 KiB
JSON
533 lines
26 KiB
JSON
|
{
|
||
|
|
"results": {
|
||
|
|
"minerva_math": {
|
||
|
|
"exact_match,none": 0.3076,
|
||
|
|
"exact_match_stderr,none": 0.006198998754660659,
|
||
|
|
"alias": "minerva_math"
|
||
|
|
},
|
||
|
|
"minerva_math_algebra": {
|
||
|
|
"alias": " - minerva_math_algebra",
|
||
|
|
"exact_match,none": 0.4026958719460826,
|
||
|
|
"exact_match_stderr,none": 0.014241115293724816
|
||
|
|
},
|
||
|
|
"minerva_math_counting_and_prob": {
|
||
|
|
"alias": " - minerva_math_counting_and_prob",
|
||
|
|
"exact_match,none": 0.350210970464135,
|
||
|
|
"exact_match_stderr,none": 0.021934133893619426
|
||
|
|
},
|
||
|
|
"minerva_math_geometry": {
|
||
|
|
"alias": " - minerva_math_geometry",
|
||
|
|
"exact_match,none": 0.3173277661795407,
|
||
|
|
"exact_match_stderr,none": 0.02128855620995171
|
||
|
|
},
|
||
|
|
"minerva_math_intermediate_algebra": {
|
||
|
|
"alias": " - minerva_math_intermediate_algebra",
|
||
|
|
"exact_match,none": 0.09745293466223699,
|
||
|
|
"exact_match_stderr,none": 0.009874818485404377
|
||
|
|
},
|
||
|
|
"minerva_math_num_theory": {
|
||
|
|
"alias": " - minerva_math_num_theory",
|
||
|
|
"exact_match,none": 0.24444444444444444,
|
||
|
|
"exact_match_stderr,none": 0.018510958396334234
|
||
|
|
},
|
||
|
|
"minerva_math_prealgebra": {
|
||
|
|
"alias": " - minerva_math_prealgebra",
|
||
|
|
"exact_match,none": 0.5120551090700345,
|
||
|
|
"exact_match_stderr,none": 0.016946659873163027
|
||
|
|
},
|
||
|
|
"minerva_math_precalc": {
|
||
|
|
"alias": " - minerva_math_precalc",
|
||
|
|
"exact_match,none": 0.1391941391941392,
|
||
|
|
"exact_match_stderr,none": 0.014827394112308778
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"groups": {
|
||
|
|
"minerva_math": {
|
||
|
|
"exact_match,none": 0.3076,
|
||
|
|
"exact_match_stderr,none": 0.006198998754660659,
|
||
|
|
"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 0x15110549ecb0>"
|
||
|
|
},
|
||
|
|
"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 0x15110549e050>"
|
||
|
|
},
|
||
|
|
"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 0x15110549dcf0>"
|
||
|
|
},
|
||
|
|
"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 0x151105491360>"
|
||
|
|
},
|
||
|
|
"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 0x151105490790>"
|
||
|
|
},
|
||
|
|
"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 0x15116fad96c0>"
|
||
|
|
},
|
||
|
|
"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 0x15116fbe83a0>"
|
||
|
|
},
|
||
|
|
"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": "pretrained=tiiuae/Falcon3-7B-Instruct,trust_remote_code=True,cache_dir=/tmp,parallelize=True",
|
||
|
|
"model_num_parameters": 7455550464,
|
||
|
|
"model_dtype": "torch.bfloat16",
|
||
|
|
"model_revision": "main",
|
||
|
|
"model_sha": "5563a370c1848366c7a095bde4bbff2cdb419cc6",
|
||
|
|
"batch_size": 1,
|
||
|
|
"batch_sizes": [],
|
||
|
|
"device": null,
|
||
|
|
"use_cache": null,
|
||
|
|
"limit": null,
|
||
|
|
"bootstrap_iters": 100000,
|
||
|
|
"gen_kwargs": null,
|
||
|
|
"random_seed": 0,
|
||
|
|
"numpy_seed": 1234,
|
||
|
|
"torch_seed": 1234,
|
||
|
|
"fewshot_seed": 1234
|
||
|
|
},
|
||
|
|
"git_hash": "5e10e017",
|
||
|
|
"date": 1736902050.8686402,
|
||
|
|
"pretty_env_info": "PyTorch version: 2.4.0+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.3 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.27.1\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-5.15.0-1064-azure-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.2.128\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\n\nNvidia driver version: 535.161.08\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.4\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 96\nOn-line CPU(s) list: 0-95\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V12 64-Core Processor\nCPU family: 23\nModel: 49\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 0\nBogoMIPS: 4890.88\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru arat umip rdpid\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 3 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 48 MiB (96 instances)\nL3 cache: 384 MiB (24 instances)\nNUMA node(s): 4\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nNUMA node2 CPU(s): 48-71\nNUMA node3 CPU(s): 72-95\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Mitigation; untrained return thunk; SMT disabled\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort:
|
||
|
|
"transformers_version": "4.48.0",
|
||
|
|
"upper_git_hash": "f64fe2f2a86055aaecced603b56097fd79201711",
|
||
|
|
"tokenizer_pad_token": [
|
||
|
|
"<|pad|>",
|
||
|
|
"2023"
|
||
|
|
],
|
||
|
|
"tokenizer_eos_token": [
|
||
|
|
"<|endoftext|>",
|
||
|
|
"11"
|
||
|
|
],
|
||
|
|
"tokenizer_bos_token": [
|
||
|
|
null,
|
||
|
|
"None"
|
||
|
|
],
|
||
|
|
"eot_token_id": 11,
|
||
|
|
"max_length": 32768,
|
||
|
|
"task_hashes": {
|
||
|
|
"minerva_math_algebra": "185f34b170fd1ddec0f7e4c6f6b46ec8e3634ad4c99d822a3e2f0a964a15f0d5",
|
||
|
|
"minerva_math_counting_and_prob": "7edba0e802d0ed4e586e3511f6cc4f7d369268a05835a9a4160e9c79236c0718",
|
||
|
|
"minerva_math_geometry": "a089b5ed647abeb1874a75b3212f265db6f797cb85a56c4ee8b6dcba00bb946f",
|
||
|
|
"minerva_math_intermediate_algebra": "1f523afc1e3a8ca005120f5c859d3ca68c7cc592bddc4d583eab99c076f188d1",
|
||
|
|
"minerva_math_num_theory": "0d8bdb3a26388da49d3e8d8419869655a3a3247dde250e368e44534cf5bba0ea",
|
||
|
|
"minerva_math_prealgebra": "27c50c162f003f7257958233b7e6501b6250cf8c580dda185ddc2f76ff9ae866",
|
||
|
|
"minerva_math_precalc": "1f27730753ee7cd62d6de902471a10a0adb5e0254b7d6014f56f459820aec022"
|
||
|
|
},
|
||
|
|
"model_source": "hf",
|
||
|
|
"model_name": "tiiuae/Falcon3-7B-Instruct",
|
||
|
|
"model_name_sanitized": "tiiuae__Falcon3-7B-Instruct",
|
||
|
|
"system_instruction": null,
|
||
|
|
"system_instruction_sha": null,
|
||
|
|
"fewshot_as_multiturn": false,
|
||
|
|
"chat_template": null,
|
||
|
|
"chat_template_sha": null,
|
||
|
|
"start_time": 612301.351469343,
|
||
|
|
"end_time": 616050.463408958,
|
||
|
|
"total_evaluation_time_seconds": "3749.1119396151043"
|
||
|
|
}
|