{ "results": { "lambada_openai": { "perplexity,none": 3611714.469681177, "perplexity_stderr,none": 349659.810942476, "acc,none": 0.0, "acc_stderr,none": 0.0, "alias": "lambada_openai" }, "hendrycks_math": { "exact_match,none": 0.0, "exact_match_stderr,none": 0.0, "alias": "hendrycks_math" }, "hendrycks_math_algebra": { "exact_match,none": 0.0, "exact_match_stderr,none": 0.0, "alias": " - hendrycks_math_algebra" }, "hendrycks_math_counting_and_prob": { "exact_match,none": 0.0, "exact_match_stderr,none": 0.0, "alias": " - hendrycks_math_counting_and_prob" }, "hendrycks_math_geometry": { "exact_match,none": 0.0, "exact_match_stderr,none": 0.0, "alias": " - hendrycks_math_geometry" }, "hendrycks_math_intermediate_algebra": { "exact_match,none": 0.0, "exact_match_stderr,none": 0.0, "alias": " - hendrycks_math_intermediate_algebra" }, "hendrycks_math_num_theory": { "exact_match,none": 0.0, "exact_match_stderr,none": 0.0, "alias": " - hendrycks_math_num_theory" }, "hendrycks_math_prealgebra": { "exact_match,none": 0.0, "exact_match_stderr,none": 0.0, "alias": " - hendrycks_math_prealgebra" }, "hendrycks_math_precalc": { "exact_match,none": 0.0, "exact_match_stderr,none": 0.0, "alias": " - hendrycks_math_precalc" } }, "groups": { "hendrycks_math": { "exact_match,none": 0.0, "exact_match_stderr,none": 0.0, "alias": "hendrycks_math" } }, "group_subtasks": { "hendrycks_math": [ "hendrycks_math_precalc", "hendrycks_math_prealgebra", "hendrycks_math_num_theory", "hendrycks_math_intermediate_algebra", "hendrycks_math_geometry", "hendrycks_math_counting_and_prob", "hendrycks_math_algebra" ], "lambada_openai": [] }, "configs": { "hendrycks_math_algebra": { "task": "hendrycks_math_algebra", "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\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", "doc_to_text": "Problem: {{problem}}\nAnswer:", "doc_to_target": "{{answer}}", "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [ { "metric": "exact_match", "aggregation": "mean", "higher_is_better": true } ], "output_type": "generate_until", "generation_kwargs": { "until": [ "Problem:" ], "do_sample": false, "temperature": 0.0 }, "repeats": 1, "should_decontaminate": false, "metadata": { "version": 1.0 } }, "hendrycks_math_counting_and_prob": { "task": "hendrycks_math_counting_and_prob", "group": [ "math_word_problems" ], "dataset_path": "EleutherAI/hendrycks_math", "dataset_name": "counting_and_probability", "dataset_kwargs": { "trust_remote_code": true }, "training_split": "train", "test_split": "test", "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", "doc_to_text": "Problem: {{problem}}\nAnswer:", "doc_to_target": "{{answer}}", "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "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 } }, "hendrycks_math_geometry": { "task": "hendrycks_math_geometry", "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\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", "doc_to_text": "Problem: {{problem}}\nAnswer:", "doc_to_target": "{{answer}}", "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [ { "metric": "exact_match", "aggregation": "mean", "higher_is_better": true } ], "output_type": "generate_until", "generation_kwargs": { "until": [ "Problem:" ], "do_sample": false, "temperature": 0.0 }, "repeats": 1, "should_decontaminate": false, "metadata": { "version": 1.0 } }, "hendrycks_math_intermediate_algebra": { "task": "hendrycks_math_intermediate_algebra", "group": [ "math_word_problems" ], "dataset_path": "EleutherAI/hendrycks_math", "dataset_name": "intermediate_algebra", "dataset_kwargs": { "trust_remote_code": true }, "training_split": "train", "test_split": "test", "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", "doc_to_text": "Problem: {{problem}}\nAnswer:", "doc_to_target": "{{answer}}", "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "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 } }, "hendrycks_math_num_theory": { "task": "hendrycks_math_num_theory", "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\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", "doc_to_text": "Problem: {{problem}}\nAnswer:", "doc_to_target": "{{answer}}", "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [ { "metric": "exact_match", "aggregation": "mean", "higher_is_better": true } ], "output_type": "generate_until", "generation_kwargs": { "until": [ "Problem:" ], "do_sample": false, "temperature": 0.0 }, "repeats": 1, "should_decontaminate": false, "metadata": { "version": 1.0 } }, "hendrycks_math_prealgebra": { "task": "hendrycks_math_prealgebra", "group": [ "math_word_problems" ], "dataset_path": "EleutherAI/hendrycks_math", "dataset_name": "prealgebra", "dataset_kwargs": { "trust_remote_code": true }, "training_split": "train", "test_split": "test", "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n out_doc = {\n \"problem\": doc[\"problem\"],\n \"solution\": doc[\"solution\"],\n \"answer\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", "doc_to_text": "Problem: {{problem}}\nAnswer:", "doc_to_target": "{{answer}}", "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "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 } }, "hendrycks_math_precalc": { "task": "hendrycks_math_precalc", "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\": remove_boxed(last_boxed_only_string(doc[\"solution\"])),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", "doc_to_text": "Problem: {{problem}}\nAnswer:", "doc_to_target": "{{answer}}", "process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n retval = 0\n indices = [pos for pos, char in enumerate(results[0]) if char == \"$\"]\n if len(indices) <= 1:\n answer = results[0]\n else:\n answer = results[0][indices[0] + 1 : indices[-1]]\n\n if is_equiv(answer, remove_boxed(last_boxed_only_string(doc[\"solution\"]))):\n retval = 1\n\n results = {\n \"exact_match\": retval,\n }\n return results\n", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "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 } }, "lambada_openai": { "task": "lambada_openai", "group": [ "lambada" ], "dataset_path": "EleutherAI/lambada_openai", "dataset_name": "default", "dataset_kwargs": { "trust_remote_code": true }, "test_split": "test", "doc_to_text": "{{text.split(' ')[:-1]|join(' ')}}", "doc_to_target": "{{' '+text.split(' ')[-1]}}", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "metric_list": [ { "metric": "perplexity", "aggregation": "perplexity", "higher_is_better": false }, { "metric": "acc", "aggregation": "mean", "higher_is_better": true } ], "output_type": "loglikelihood", "repeats": 1, "should_decontaminate": true, "doc_to_decontamination_query": "{{text}}", "metadata": { "version": 1.0 } } }, "versions": { "hendrycks_math_algebra": 1.0, "hendrycks_math_counting_and_prob": 1.0, "hendrycks_math_geometry": 1.0, "hendrycks_math_intermediate_algebra": 1.0, "hendrycks_math_num_theory": 1.0, "hendrycks_math_prealgebra": 1.0, "hendrycks_math_precalc": 1.0, "lambada_openai": 1.0 }, "n-shot": { "hendrycks_math": 0, "hendrycks_math_algebra": 0, "hendrycks_math_counting_and_prob": 0, "hendrycks_math_geometry": 0, "hendrycks_math_intermediate_algebra": 0, "hendrycks_math_num_theory": 0, "hendrycks_math_prealgebra": 0, "hendrycks_math_precalc": 0, "lambada_openai": 0 }, "n-samples": { "lambada_openai": { "original": 5153, "effective": 5153 }, "hendrycks_math_precalc": { "original": 546, "effective": 546 }, "hendrycks_math_prealgebra": { "original": 871, "effective": 871 }, "hendrycks_math_num_theory": { "original": 540, "effective": 540 }, "hendrycks_math_intermediate_algebra": { "original": 903, "effective": 903 }, "hendrycks_math_geometry": { "original": 479, "effective": 479 }, "hendrycks_math_counting_and_prob": { "original": 474, "effective": 474 }, "hendrycks_math_algebra": { "original": 1187, "effective": 1187 } }, "config": { "model": "hf", "model_args": "pretrained=EleutherAI/pythia-14m-seed2,revision=step1,", "model_num_parameters": 14067712, "model_dtype": "torch.float16", "model_revision": "step1", "model_sha": "3c114397768108a01b3fa9395bd3c607abef1f06", "batch_size": "128", "batch_sizes": [], "device": "cuda", "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": "51a7ca9", "date": 1723460881.4871593, "pretty_env_info": "PyTorch version: 2.3.0+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: CentOS Linux release 7.9.2009 (Core) (x86_64)\nGCC version: (GCC) 12.1.0\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.17\n\nPython version: 3.12.1 (main, Jan 12 2024, 16:49:08) [GCC 12.1.0] (64-bit runtime)\nPython platform: Linux-3.10.0-1160.119.1.el7.tuxcare.els2.x86_64-x86_64-with-glibc2.17\nIs CUDA available: True\nCUDA runtime version: 12.4.99\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: GPU 0: Tesla V100S-PCIE-32GB\nNvidia driver version: 550.90.07\ncuDNN version: Could not collect\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\nByte Order: Little Endian\nCPU(s): 32\nOn-line CPU(s) list: 0-31\nThread(s) per core: 1\nCore(s) per socket: 32\nSocket(s): 1\nNUMA node(s): 2\nVendor ID: AuthenticAMD\nCPU family: 23\nModel: 49\nModel name: AMD EPYC 7502P 32-Core Processor\nStepping: 0\nCPU MHz: 1500.000\nCPU max MHz: 2500.0000\nCPU min MHz: 1500.0000\nBogoMIPS: 5000.08\nVirtualization: AMD-V\nL1d cache: 32K\nL1i cache: 32K\nL2 cache: 512K\nL3 cache: 16384K\nNUMA node0 CPU(s): 0-15\nNUMA node1 CPU(s): 16-31\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 art rep_good nopl nonstop_tsc extd_apicid aperfmperf eagerfpu pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_l2 cpb cat_l3 cdp_l3 hw_pstate sme ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif umip overflow_recov succor smca\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] torch==2.3.0\n[conda] Could not collect", "transformers_version": "4.40.2", "upper_git_hash": null, "task_hashes": {}, "model_source": "hf", "model_name": "EleutherAI/pythia-14m-seed2", "model_name_sanitized": "EleutherAI__pythia-14m-seed2", "start_time": 843447.295591911, "end_time": 843684.802160944, "total_evaluation_time_seconds": "237.50656903302297" }