cot-eval-results
/
data
/Qwen
/Qwen2-72B-Instruct
/base
/24-09-19-06:23:13
/Qwen__Qwen2-72B-Instruct
/results_2024-09-19T06-42-20.862136.json
| { | |
| "results": { | |
| "eaque-amet-7166_lsat-rc_base": { | |
| "acc,none": 0.6728624535315985, | |
| "acc_stderr,none": 0.028658994326690786, | |
| "alias": "eaque-amet-7166_lsat-rc_base" | |
| }, | |
| "eaque-amet-7166_lsat-lr_base": { | |
| "acc,none": 0.5823529411764706, | |
| "acc_stderr,none": 0.021859436336153608, | |
| "alias": "eaque-amet-7166_lsat-lr_base" | |
| }, | |
| "eaque-amet-7166_lsat-ar_base": { | |
| "acc,none": 0.29130434782608694, | |
| "acc_stderr,none": 0.030025180463241888, | |
| "alias": "eaque-amet-7166_lsat-ar_base" | |
| }, | |
| "eaque-amet-7166_logiqa_base": { | |
| "acc,none": 0.41533546325878595, | |
| "acc_stderr,none": 0.01971119138838218, | |
| "alias": "eaque-amet-7166_logiqa_base" | |
| }, | |
| "eaque-amet-7166_logiqa2_base": { | |
| "acc,none": 0.589058524173028, | |
| "acc_stderr,none": 0.012413124695985664, | |
| "alias": "eaque-amet-7166_logiqa2_base" | |
| } | |
| }, | |
| "group_subtasks": { | |
| "eaque-amet-7166_logiqa2_base": [], | |
| "eaque-amet-7166_logiqa_base": [], | |
| "eaque-amet-7166_lsat-ar_base": [], | |
| "eaque-amet-7166_lsat-lr_base": [], | |
| "eaque-amet-7166_lsat-rc_base": [] | |
| }, | |
| "configs": { | |
| "eaque-amet-7166_logiqa2_base": { | |
| "task": "eaque-amet-7166_logiqa2_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "cot-leaderboard/cot-eval-traces-2.0", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "data/Qwen/Qwen2-72B-Instruct/eaque-amet-7166-logiqa2.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\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 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.\\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 += \"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 | |
| } | |
| }, | |
| "eaque-amet-7166_logiqa_base": { | |
| "task": "eaque-amet-7166_logiqa_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "cot-leaderboard/cot-eval-traces-2.0", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "data/Qwen/Qwen2-72B-Instruct/eaque-amet-7166-logiqa.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\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 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.\\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 += \"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 | |
| } | |
| }, | |
| "eaque-amet-7166_lsat-ar_base": { | |
| "task": "eaque-amet-7166_lsat-ar_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "cot-leaderboard/cot-eval-traces-2.0", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "data/Qwen/Qwen2-72B-Instruct/eaque-amet-7166-lsat-ar.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\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 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.\\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 += \"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 | |
| } | |
| }, | |
| "eaque-amet-7166_lsat-lr_base": { | |
| "task": "eaque-amet-7166_lsat-lr_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "cot-leaderboard/cot-eval-traces-2.0", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "data/Qwen/Qwen2-72B-Instruct/eaque-amet-7166-lsat-lr.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\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 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.\\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 += \"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 | |
| } | |
| }, | |
| "eaque-amet-7166_lsat-rc_base": { | |
| "task": "eaque-amet-7166_lsat-rc_base", | |
| "group": "logikon-bench", | |
| "dataset_path": "cot-leaderboard/cot-eval-traces-2.0", | |
| "dataset_kwargs": { | |
| "data_files": { | |
| "test": "data/Qwen/Qwen2-72B-Instruct/eaque-amet-7166-lsat-rc.parquet" | |
| } | |
| }, | |
| "test_split": "test", | |
| "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\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 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.\\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 += \"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": { | |
| "eaque-amet-7166_logiqa2_base": 0.0, | |
| "eaque-amet-7166_logiqa_base": 0.0, | |
| "eaque-amet-7166_lsat-ar_base": 0.0, | |
| "eaque-amet-7166_lsat-lr_base": 0.0, | |
| "eaque-amet-7166_lsat-rc_base": 0.0 | |
| }, | |
| "n-shot": { | |
| "eaque-amet-7166_logiqa2_base": 0, | |
| "eaque-amet-7166_logiqa_base": 0, | |
| "eaque-amet-7166_lsat-ar_base": 0, | |
| "eaque-amet-7166_lsat-lr_base": 0, | |
| "eaque-amet-7166_lsat-rc_base": 0 | |
| }, | |
| "higher_is_better": { | |
| "eaque-amet-7166_logiqa2_base": { | |
| "acc": true | |
| }, | |
| "eaque-amet-7166_logiqa_base": { | |
| "acc": true | |
| }, | |
| "eaque-amet-7166_lsat-ar_base": { | |
| "acc": true | |
| }, | |
| "eaque-amet-7166_lsat-lr_base": { | |
| "acc": true | |
| }, | |
| "eaque-amet-7166_lsat-rc_base": { | |
| "acc": true | |
| } | |
| }, | |
| "n-samples": { | |
| "eaque-amet-7166_lsat-rc_base": { | |
| "original": 269, | |
| "effective": 269 | |
| }, | |
| "eaque-amet-7166_lsat-lr_base": { | |
| "original": 510, | |
| "effective": 510 | |
| }, | |
| "eaque-amet-7166_lsat-ar_base": { | |
| "original": 230, | |
| "effective": 230 | |
| }, | |
| "eaque-amet-7166_logiqa_base": { | |
| "original": 626, | |
| "effective": 626 | |
| }, | |
| "eaque-amet-7166_logiqa2_base": { | |
| "original": 1572, | |
| "effective": 1572 | |
| } | |
| }, | |
| "config": { | |
| "model": "vllm", | |
| "model_args": "pretrained=Qwen/Qwen2-72B-Instruct,revision=main,dtype=bfloat16,tensor_parallel_size=4,gpu_memory_utilization=0.7,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, | |
| "random_seed": 0, | |
| "numpy_seed": 1234, | |
| "torch_seed": 1234, | |
| "fewshot_seed": 1234 | |
| }, | |
| "git_hash": "5df942c", | |
| "date": 1726720384.7380302, | |
| "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 22.04.4 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 3.29.2\nLibc version: glibc-2.35\n\nPython version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-4.18.0-477.70.1.el8_8.x86_64-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.4.131\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA H100\nGPU 1: NVIDIA H100\nGPU 2: NVIDIA H100\nGPU 3: NVIDIA H100\n\nNvidia driver version: 550.54.15\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.1.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.1.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.1.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.1.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.1.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.1.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.1.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.1.0\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: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 128\nOn-line CPU(s) list: 0-127\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 9354 32-Core Processor\nCPU family: 25\nModel: 17\nThread(s) per core: 2\nCore(s) per socket: 32\nSocket(s): 2\nStepping: 1\nFrequency boost: enabled\nCPU max MHz: 3800.0000\nCPU min MHz: 400.0000\nBogoMIPS: 6500.03\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 pcid 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 invpcid_single hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d\nVirtualization: AMD-V\nL1d cache: 2 MiB (64 instances)\nL1i cache: 2 MiB (64 instances)\nL2 cache: 64 MiB (64 instances)\nL3 cache: 512 MiB (16 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-31,64-95\nNUMA node1 CPU(s): 32-63,96-127\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\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, IBRS_FW, 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] flashinfer==0.1.6+cu124torch2.4\n[pip3] mypy-extensions==1.0.0\n[pip3] numpy==1.24.4\n[pip3] onnx==1.16.0\n[pip3] optree==0.11.0\n[pip3] pytorch-quantization==2.1.2\n[pip3] pytorch-triton==3.0.0+989adb9a2\n[pip3] torch==2.4.0\n[pip3] torch-tensorrt==2.4.0a0\n[pip3] torchvision==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect", | |
| "transformers_version": "4.44.2", | |
| "upper_git_hash": null, | |
| "tokenizer_pad_token": [ | |
| "<|endoftext|>", | |
| 151643 | |
| ], | |
| "tokenizer_eos_token": [ | |
| "<|im_end|>", | |
| 151645 | |
| ], | |
| "tokenizer_bos_token": [ | |
| null, | |
| null | |
| ], | |
| "eot_token_id": 151645, | |
| "max_length": 2048, | |
| "task_hashes": {}, | |
| "model_source": "vllm", | |
| "model_name": "Qwen/Qwen2-72B-Instruct", | |
| "model_name_sanitized": "Qwen__Qwen2-72B-Instruct", | |
| "system_instruction": null, | |
| "system_instruction_sha": null, | |
| "fewshot_as_multiturn": false, | |
| "chat_template": null, | |
| "chat_template_sha": null, | |
| "start_time": 242167.38498714, | |
| "end_time": 242727.195304418, | |
| "total_evaluation_time_seconds": "559.8103172780247" | |
| } |