| { | |
| "results": { | |
| "cumque-molestias-1146_lsat-rc_cot": { | |
| "acc,none": 0.2788104089219331, | |
| "acc_stderr,none": 0.0273912479757104, | |
| "alias": "cumque-molestias-1146_lsat-rc_cot" | |
| }, | |
| "cumque-molestias-1146_lsat-lr_cot": { | |
| "acc,none": 0.2549019607843137, | |
| "acc_stderr,none": 0.01931676548053297, | |
| "alias": "cumque-molestias-1146_lsat-lr_cot" | |
| }, | |
| "cumque-molestias-1146_lsat-ar_cot": { | |
| "acc,none": 0.1782608695652174, | |
| "acc_stderr,none": 0.025291655246273914, | |
| "alias": "cumque-molestias-1146_lsat-ar_cot" | |
| }, | |
| "cumque-molestias-1146_logiqa_cot": { | |
| "acc,none": 0.2747603833865815, | |
| "acc_stderr,none": 0.017855738130151344, | |
| "alias": "cumque-molestias-1146_logiqa_cot" | |
| }, | |
| "cumque-molestias-1146_logiqa2_cot": { | |
| "acc,none": 0.3237913486005089, | |
| "acc_stderr,none": 0.011805513691277374, | |
| "alias": "cumque-molestias-1146_logiqa2_cot" | |
| } | |
| }, | |
| "group_subtasks": { | |
| "cumque-molestias-1146_logiqa2_cot": [], | |
| "cumque-molestias-1146_logiqa_cot": [], | |
| "cumque-molestias-1146_lsat-ar_cot": [], | |
| "cumque-molestias-1146_lsat-lr_cot": [], | |
| "cumque-molestias-1146_lsat-rc_cot": [] | |
| }, | |
| "configs": { | |
| "cumque-molestias-1146_logiqa2_cot": { | |
| "task": "cumque-molestias-1146_logiqa2_cot", | |
| "group": "logikon-bench", | |
| "dataset_path": "cot-leaderboard/cot-eval-traces-2.0", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "data/meta-llama/Meta-Llama-3-8B/cumque-molestias-1146-logiqa2.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "cumque-molestias-1146_logiqa_cot": { | |
| "task": "cumque-molestias-1146_logiqa_cot", | |
| "group": "logikon-bench", | |
| "dataset_path": "cot-leaderboard/cot-eval-traces-2.0", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "data/meta-llama/Meta-Llama-3-8B/cumque-molestias-1146-logiqa.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "cumque-molestias-1146_lsat-ar_cot": { | |
| "task": "cumque-molestias-1146_lsat-ar_cot", | |
| "group": "logikon-bench", | |
| "dataset_path": "cot-leaderboard/cot-eval-traces-2.0", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "data/meta-llama/Meta-Llama-3-8B/cumque-molestias-1146-lsat-ar.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "cumque-molestias-1146_lsat-lr_cot": { | |
| "task": "cumque-molestias-1146_lsat-lr_cot", | |
| "group": "logikon-bench", | |
| "dataset_path": "cot-leaderboard/cot-eval-traces-2.0", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "data/meta-llama/Meta-Llama-3-8B/cumque-molestias-1146-lsat-lr.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| }, | |
| "cumque-molestias-1146_lsat-rc_cot": { | |
| "task": "cumque-molestias-1146_lsat-rc_cot", | |
| "group": "logikon-bench", | |
| "dataset_path": "cot-leaderboard/cot-eval-traces-2.0", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "data/meta-llama/Meta-Llama-3-8B/cumque-molestias-1146-lsat-rc.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n [Reasoning: <reasoning>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage. Base your answer on the reasoning below.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n", | |
| "doc_to_target": "{{answer}}", | |
| "doc_to_choice": "{{options}}", | |
| "description": "", | |
| "target_delimiter": " ", | |
| "fewshot_delimiter": "\n\n", | |
| "num_fewshot": 0, | |
| "metric_list": [ | |
| { | |
| "metric": "acc", | |
| "aggregation": "mean", | |
| "higher_is_better": true | |
| } | |
| ], | |
| "output_type": "multiple_choice", | |
| "repeats": 1, | |
| "should_decontaminate": false, | |
| "metadata": { | |
| "version": 0.0 | |
| } | |
| } | |
| }, | |
| "versions": { | |
| "cumque-molestias-1146_logiqa2_cot": 0.0, | |
| "cumque-molestias-1146_logiqa_cot": 0.0, | |
| "cumque-molestias-1146_lsat-ar_cot": 0.0, | |
| "cumque-molestias-1146_lsat-lr_cot": 0.0, | |
| "cumque-molestias-1146_lsat-rc_cot": 0.0 | |
| }, | |
| "n-shot": { | |
| "cumque-molestias-1146_logiqa2_cot": 0, | |
| "cumque-molestias-1146_logiqa_cot": 0, | |
| "cumque-molestias-1146_lsat-ar_cot": 0, | |
| "cumque-molestias-1146_lsat-lr_cot": 0, | |
| "cumque-molestias-1146_lsat-rc_cot": 0 | |
| }, | |
| "config": { | |
| "model": "vllm", | |
| "model_args": "pretrained=meta-llama/Meta-Llama-3-8B,revision=main,dtype=bfloat16,tensor_parallel_size=1,gpu_memory_utilization=0.8,trust_remote_code=true,max_length=2048", | |
| "batch_size": "auto", | |
| "batch_sizes": [], | |
| "device": null, | |
| "use_cache": null, | |
| "limit": null, | |
| "bootstrap_iters": 100000, | |
| "gen_kwargs": null | |
| }, | |
| "git_hash": "f3c749c", | |
| "date": 1714372013.872163, | |
| "pretty_env_info": "PyTorch version: 2.1.2+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 3.27.6\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-4.18.0-477.51.1.el8_8.x86_64-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.2.140\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: GPU 0: NVIDIA A100-SXM4-40GB\nNvidia driver version: 550.54.15\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.5\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: 43 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 256\nOn-line CPU(s) list: 0-255\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7742 64-Core Processor\nCPU family: 23\nModel: 49\nThread(s) per core: 2\nCore(s) per socket: 64\nSocket(s): 2\nStepping: 0\nFrequency boost: enabled\nCPU max MHz: 2250.0000\nCPU min MHz: 1500.0000\nBogoMIPS: 4491.31\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 rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf 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_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es\nVirtualization: AMD-V\nL1d cache: 4 MiB (128 instances)\nL1i cache: 4 MiB (128 instances)\nL2 cache: 64 MiB (128 instances)\nL3 cache: 512 MiB (32 instances)\nNUMA node(s): 8\nNUMA node0 CPU(s): 0-15,128-143\nNUMA node1 CPU(s): 16-31,144-159\nNUMA node2 CPU(s): 32-47,160-175\nNUMA node3 CPU(s): 48-63,176-191\nNUMA node4 CPU(s): 64-79,192-207\nNUMA node5 CPU(s): 80-95,208-223\nNUMA node6 CPU(s): 96-111,224-239\nNUMA node7 CPU(s): 112-127,240-255\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 enabled with STIBP protection\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] mypy-extensions==1.0.0\n[pip3] numpy==1.22.2\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.1.2\n[pip3] torch-tensorrt==0.0.0\n[pip3] torchdata==0.7.0a0\n[pip3] torchtext==0.16.0a0\n[pip3] torchvision==0.16.0a0\n[pip3] triton==2.1.0+e621604\n[conda] Could not collect", | |
| "transformers_version": "4.40.0", | |
| "upper_git_hash": null | |
| } |