521 lines
25 KiB
JSON
521 lines
25 KiB
JSON
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{
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"results": {
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"minerva_math": {
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"alias": "minerva_math"
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},
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"minerva_math_algebra": {
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"alias": " - minerva_math_algebra",
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"exact_match_stderr,none": 0.013546762042128943
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},
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"minerva_math_counting_and_prob": {
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"alias": " - minerva_math_counting_and_prob",
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"exact_match,none": 0.2742616033755274,
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"exact_match_stderr,none": 0.02051360484406738
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"minerva_math_geometry": {
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"alias": " - minerva_math_geometry",
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"exact_match,none": 0.21920668058455114,
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"exact_match_stderr,none": 0.01892260783793806
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"minerva_math_intermediate_algebra": {
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"alias": " - minerva_math_intermediate_algebra",
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"exact_match,none": 0.10299003322259136,
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"exact_match_stderr,none": 0.010120290165653793
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"minerva_math_num_theory": {
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"alias": " - minerva_math_num_theory",
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"exact_match_stderr,none": 0.014199721587639907
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"minerva_math_prealgebra": {
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"alias": " - minerva_math_prealgebra",
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"exact_match,none": 0.3593570608495982,
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"exact_match_stderr,none": 0.016267150584018796
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"minerva_math_precalc": {
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"alias": " - minerva_math_precalc",
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"exact_match,none": 0.11721611721611722,
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"exact_match_stderr,none": 0.01377915584962479
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}
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},
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"groups": {
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"minerva_math": {
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"exact_match,none": 0.2304,
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"exact_match_stderr,none": 0.0057791882007822044,
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"alias": "minerva_math"
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}
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},
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"group_subtasks": {
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"minerva_math": [
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"minerva_math_algebra",
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"minerva_math_counting_and_prob",
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"minerva_math_geometry",
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"minerva_math_intermediate_algebra",
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"minerva_math_num_theory",
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"minerva_math_prealgebra",
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"minerva_math_precalc"
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]
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},
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"configs": {
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"minerva_math_algebra": {
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"task": "minerva_math_algebra",
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"tag": [
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"math_word_problems"
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],
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"group": [
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"math_word_problems"
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],
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"dataset_path": "EleutherAI/hendrycks_math",
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"dataset_name": "algebra",
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"dataset_kwargs": {
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"trust_remote_code": true
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},
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"training_split": "train",
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"test_split": "test",
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"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",
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"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
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"doc_to_target": "{{answer if few_shot is undefined else solution}}",
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"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",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"fewshot_config": {
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"sampler": "first_n",
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"samples": "<function list_fewshot_samples at 0x14ff4e025630>"
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},
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"num_fewshot": 4,
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"metric_list": [
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{
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"metric": "exact_match",
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"aggregation": "mean",
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"higher_is_better": true
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}
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],
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"output_type": "generate_until",
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"generation_kwargs": {
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"until": [
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"Problem:"
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],
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"do_sample": false,
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"temperature": 0.0
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},
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"repeats": 1,
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"should_decontaminate": false,
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"metadata": {
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"version": 1.0
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}
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},
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"minerva_math_counting_and_prob": {
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"task": "minerva_math_counting_and_prob",
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"tag": [
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"math_word_problems"
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],
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"group": [
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"math_word_problems"
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],
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"dataset_path": "EleutherAI/hendrycks_math",
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"dataset_name": "counting_and_probability",
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"dataset_kwargs": {
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"trust_remote_code": true
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},
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"training_split": "train",
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"test_split": "test",
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"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",
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"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
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"doc_to_target": "{{answer if few_shot is undefined else solution}}",
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"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",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"fewshot_config": {
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"sampler": "first_n",
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"samples": "<function list_fewshot_samples at 0x14ff4e06b5b0>"
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},
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"num_fewshot": 4,
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"metric_list": [
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{
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"metric": "exact_match",
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"aggregation": "mean",
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"higher_is_better": true
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}
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],
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"output_type": "generate_until",
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"generation_kwargs": {
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"until": [
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"Problem:"
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],
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"do_sample": false,
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"temperature": 0.0
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},
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"repeats": 1,
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"should_decontaminate": false,
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"metadata": {
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"version": 1.0
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}
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},
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"minerva_math_geometry": {
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"task": "minerva_math_geometry",
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"tag": [
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"math_word_problems"
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],
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"group": [
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"math_word_problems"
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],
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"dataset_path": "EleutherAI/hendrycks_math",
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"dataset_name": "geometry",
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"dataset_kwargs": {
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"trust_remote_code": true
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},
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"training_split": "train",
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"test_split": "test",
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"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",
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"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
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"doc_to_target": "{{answer if few_shot is undefined else solution}}",
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"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",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"fewshot_config": {
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"sampler": "first_n",
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"samples": "<function list_fewshot_samples at 0x14ff4e068790>"
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},
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"num_fewshot": 4,
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"metric_list": [
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{
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"metric": "exact_match",
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"aggregation": "mean",
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"higher_is_better": true
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}
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],
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"output_type": "generate_until",
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"generation_kwargs": {
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"until": [
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"Problem:"
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],
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"do_sample": false,
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"temperature": 0.0
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},
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"repeats": 1,
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"should_decontaminate": false,
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"metadata": {
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"version": 1.0
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}
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},
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"minerva_math_intermediate_algebra": {
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"task": "minerva_math_intermediate_algebra",
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"tag": [
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"math_word_problems"
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],
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"group": [
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"math_word_problems"
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],
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"dataset_path": "EleutherAI/hendrycks_math",
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"dataset_name": "intermediate_algebra",
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"dataset_kwargs": {
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"trust_remote_code": true
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},
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"training_split": "train",
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"test_split": "test",
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"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",
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"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
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"doc_to_target": "{{answer if few_shot is undefined else solution}}",
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"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",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"fewshot_config": {
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"sampler": "first_n",
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"samples": "<function list_fewshot_samples at 0x14ff4ed75a20>"
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},
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"num_fewshot": 4,
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"metric_list": [
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{
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"metric": "exact_match",
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"aggregation": "mean",
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"higher_is_better": true
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}
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],
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"output_type": "generate_until",
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"generation_kwargs": {
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"until": [
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"Problem:"
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],
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"do_sample": false,
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"temperature": 0.0
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},
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"repeats": 1,
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"should_decontaminate": false,
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"metadata": {
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"version": 1.0
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}
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},
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"minerva_math_num_theory": {
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"task": "minerva_math_num_theory",
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"tag": [
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"math_word_problems"
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],
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"group": [
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"math_word_problems"
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],
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"dataset_path": "EleutherAI/hendrycks_math",
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"dataset_name": "number_theory",
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"dataset_kwargs": {
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"trust_remote_code": true
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},
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"training_split": "train",
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"test_split": "test",
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"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",
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"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
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"doc_to_target": "{{answer if few_shot is undefined else solution}}",
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"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",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"fewshot_config": {
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"sampler": "first_n",
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"samples": "<function list_fewshot_samples at 0x14ff4ed74940>"
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},
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"num_fewshot": 4,
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"metric_list": [
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{
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"metric": "exact_match",
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"aggregation": "mean",
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"higher_is_better": true
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}
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],
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"output_type": "generate_until",
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"generation_kwargs": {
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"until": [
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"Problem:"
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],
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"do_sample": false,
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"temperature": 0.0
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},
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"repeats": 1,
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"should_decontaminate": false,
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"metadata": {
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"version": 1.0
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}
|
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},
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"minerva_math_prealgebra": {
|
||
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"task": "minerva_math_prealgebra",
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"tag": [
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"math_word_problems"
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],
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"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 0x14ff4f4f6950>"
|
||
|
|
},
|
||
|
|
"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 0x14ff4f425120>"
|
||
|
|
},
|
||
|
|
"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": "vllm",
|
||
|
|
"model_args": "pretrained=Qwen/Qwen2.5-14B-Instruct,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,
|
||
|
|
"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": "150ae04f",
|
||
|
|
"date": 1737553245.7763708,
|
||
|
|
"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 80GB PCIe\nGPU 1: NVIDIA A100 80GB PCIe\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): 48\nOn-line CPU(s) list: 0-47\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V13 64-Core Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 1\nStepping: 1\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 pcid 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 invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat umip vaes vpclmulqdq rdpid fsrm\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 1.5 MiB (48 instances)\nL1i cache: 1.5 MiB (48 instances)\nL2 cache: 24 MiB (48 instances)\nL3 cache: 192 MiB (6 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\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, no microcode\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] onnx==1.14.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\n[pip3] torch-tensorrt==2.0.0.dev0\n[pip3] torchaudio==2.1.0\n[pip3] torchdata==0.7.0a0\n[pip3] torchmetrics==1.2.
|
||
|
|
"transformers_version": "4.48.1",
|
||
|
|
"upper_git_hash": "086919bd66f4e15fdcd4b792a7b27a698c1ba091",
|
||
|
|
"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": "vllm",
|
||
|
|
"model_name": "Qwen/Qwen2.5-14B-Instruct",
|
||
|
|
"model_name_sanitized": "Qwen__Qwen2.5-14B-Instruct",
|
||
|
|
"system_instruction": null,
|
||
|
|
"system_instruction_sha": null,
|
||
|
|
"fewshot_as_multiturn": false,
|
||
|
|
"chat_template": null,
|
||
|
|
"chat_template_sha": null,
|
||
|
|
"start_time": 30790.798752242,
|
||
|
|
"end_time": 31485.130699311,
|
||
|
|
"total_evaluation_time_seconds": "694.3319470690003"
|
||
|
|
}
|