| { | |
| "results": { | |
| "minerva_math": { | |
| "exact_match,none": 0.173, | |
| "exact_match_stderr,none": 0.005146622162421542, | |
| "alias": "minerva_math" | |
| }, | |
| "minerva_math_algebra": { | |
| "alias": " - minerva_math_algebra", | |
| "exact_match,none": 0.2409435551811289, | |
| "exact_match_stderr,none": 0.012418019817467794 | |
| }, | |
| "minerva_math_counting_and_prob": { | |
| "alias": " - minerva_math_counting_and_prob", | |
| "exact_match,none": 0.17088607594936708, | |
| "exact_match_stderr,none": 0.01730732195419626 | |
| }, | |
| "minerva_math_geometry": { | |
| "alias": " - minerva_math_geometry", | |
| "exact_match,none": 0.12108559498956159, | |
| "exact_match_stderr,none": 0.014921262921998898 | |
| }, | |
| "minerva_math_intermediate_algebra": { | |
| "alias": " - minerva_math_intermediate_algebra", | |
| "exact_match,none": 0.053156146179401995, | |
| "exact_match_stderr,none": 0.00746986334739643 | |
| }, | |
| "minerva_math_num_theory": { | |
| "alias": " - minerva_math_num_theory", | |
| "exact_match,none": 0.11296296296296296, | |
| "exact_match_stderr,none": 0.013634666880074295 | |
| }, | |
| "minerva_math_prealgebra": { | |
| "alias": " - minerva_math_prealgebra", | |
| "exact_match,none": 0.34328358208955223, | |
| "exact_match_stderr,none": 0.01609740338728602 | |
| }, | |
| "minerva_math_precalc": { | |
| "alias": " - minerva_math_precalc", | |
| "exact_match,none": 0.05860805860805861, | |
| "exact_match_stderr,none": 0.010061567725278785 | |
| } | |
| }, | |
| "groups": { | |
| "minerva_math": { | |
| "exact_match,none": 0.173, | |
| "exact_match_stderr,none": 0.005146622162421542, | |
| "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 0x148513aa0f70>" | |
| }, | |
| "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 0x148510eeaef0>" | |
| }, | |
| "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 0x148510ee8ca0>" | |
| }, | |
| "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 0x148510ee17e0>" | |
| }, | |
| "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 0x148510ee15a0>" | |
| }, | |
| "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 0x148510e02b90>" | |
| }, | |
| "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 0x148516fe49d0>" | |
| }, | |
| "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=/tmp/7b-alpha-v1.27.2.25,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": "788a3672", | |
| "date": 1737544396.9634442, | |
| "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==1.2.0\n[pip3] torchvision==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect", | |
| "transformers_version": "4.48.1", | |
| "upper_git_hash": null, | |
| "tokenizer_pad_token": [ | |
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| "tokenizer_eos_token": [ | |
| "</s>", | |
| "2" | |
| ], | |
| "tokenizer_bos_token": [ | |
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| ], | |
| "eot_token_id": 2, | |
| "max_length": 4096, | |
| "task_hashes": {}, | |
| "model_source": "vllm", | |
| "model_name": "/tmp/7b-alpha-v1.27.2.25", | |
| "model_name_sanitized": "__tmp__7b-alpha-v1.27.2.25", | |
| "system_instruction": null, | |
| "system_instruction_sha": null, | |
| "fewshot_as_multiturn": false, | |
| "chat_template": null, | |
| "chat_template_sha": null, | |
| "start_time": 21941.885116993, | |
| "end_time": 22486.922181144, | |
| "total_evaluation_time_seconds": "545.0370641510017" | |
| } |