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{
"results": {
"leaderboard_musr": {
" ": " ",
"alias": "leaderboard_musr"
},
"leaderboard_musr_murder_mysteries": {
"alias": " - leaderboard_musr_murder_mysteries",
"acc_norm,none": 0.54,
"acc_norm_stderr,none": 0.03158465389149902
},
"leaderboard_musr_object_placements": {
"alias": " - leaderboard_musr_object_placements",
"acc_norm,none": 0.234375,
"acc_norm_stderr,none": 0.02652733398834892
},
"leaderboard_musr_team_allocation": {
"alias": " - leaderboard_musr_team_allocation",
"acc_norm,none": 0.32,
"acc_norm_stderr,none": 0.029561724955241033
}
},
"group_subtasks": {
"leaderboard_musr": [
"leaderboard_musr_murder_mysteries",
"leaderboard_musr_object_placements",
"leaderboard_musr_team_allocation"
]
},
"configs": {
"leaderboard_musr_murder_mysteries": {
"task": "leaderboard_musr_murder_mysteries",
"dataset_path": "TAUR-Lab/MuSR",
"test_split": "murder_mysteries",
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Convert a doc to text.\n \"\"\"\n choices = \"\"\n for i, choice in enumerate(ast.literal_eval(doc[\"choices\"])):\n choices += f\"{i+1} - {choice}\\n\"\n\n text = DOC_TO_TEXT.format(\n narrative=doc[\"narrative\"], question=doc[\"question\"], choices=choices\n )\n\n return text\n",
"doc_to_target": "{{answer_choice}}",
"doc_to_choice": "{{choices}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc_norm",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"leaderboard_musr_object_placements": {
"task": "leaderboard_musr_object_placements",
"dataset_path": "TAUR-Lab/MuSR",
"test_split": "object_placements",
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Convert a doc to text.\n \"\"\"\n choices = \"\"\n for i, choice in enumerate(ast.literal_eval(doc[\"choices\"])):\n choices += f\"{i+1} - {choice}\\n\"\n\n text = DOC_TO_TEXT.format(\n narrative=doc[\"narrative\"], question=doc[\"question\"], choices=choices\n )\n\n return text\n",
"doc_to_target": "{{answer_choice}}",
"doc_to_choice": "{{choices}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc_norm",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"leaderboard_musr_team_allocation": {
"task": "leaderboard_musr_team_allocation",
"dataset_path": "TAUR-Lab/MuSR",
"test_split": "team_allocation",
"doc_to_text": "def doc_to_text(doc):\n \"\"\"\n Convert a doc to text.\n \"\"\"\n choices = \"\"\n for i, choice in enumerate(ast.literal_eval(doc[\"choices\"])):\n choices += f\"{i+1} - {choice}\\n\"\n\n text = DOC_TO_TEXT.format(\n narrative=doc[\"narrative\"], question=doc[\"question\"], choices=choices\n )\n\n return text\n",
"doc_to_target": "{{answer_choice}}",
"doc_to_choice": "{{choices}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc_norm",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
}
},
"versions": {
"leaderboard_musr_murder_mysteries": 1.0,
"leaderboard_musr_object_placements": 1.0,
"leaderboard_musr_team_allocation": 1.0
},
"n-shot": {
"leaderboard_musr_murder_mysteries": 0,
"leaderboard_musr_object_placements": 0,
"leaderboard_musr_team_allocation": 0
},
"higher_is_better": {
"leaderboard_musr": {
"acc_norm": true
},
"leaderboard_musr_murder_mysteries": {
"acc_norm": true
},
"leaderboard_musr_object_placements": {
"acc_norm": true
},
"leaderboard_musr_team_allocation": {
"acc_norm": true
}
},
"n-samples": {
"leaderboard_musr_murder_mysteries": {
"original": 250,
"effective": 250
},
"leaderboard_musr_object_placements": {
"original": 256,
"effective": 256
},
"leaderboard_musr_team_allocation": {
"original": 250,
"effective": 250
}
},
"config": {
"model": "hf",
"model_args": "pretrained=DevQuasar/analytical_reasoning_r16a32_unsloth-Llama-3.2-3B-Instruct-bnb-4bit",
"batch_size": "auto:4",
"batch_sizes": [
16,
16,
16,
32
],
"device": null,
"use_cache": "eval_cache",
"limit": null,
"bootstrap_iters": 100000,
"gen_kwargs": null,
"random_seed": 0,
"numpy_seed": 1234,
"torch_seed": 1234,
"fewshot_seed": 1234
},
"git_hash": "0230356",
"date": 1732986471.4917576,
"pretty_env_info": "PyTorch version: 2.5.1+cu124\nIs debug build: False\nCUDA used to build PyTorch: 12.4\nROCM used to build PyTorch: N/A\n\nOS: Debian GNU/Linux 12 (bookworm) (x86_64)\nGCC version: (Debian 12.2.0-14) 12.2.0\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.36\n\nPython version: 3.11.10 (main, Oct 3 2024, 07:29:13) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.1.0-26-amd64-x86_64-with-glibc2.36\nIs CUDA available: True\nCUDA runtime version: Could not collect\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA GeForce GTX 1050 Ti\nGPU 1: Tesla P40\nGPU 2: Tesla V100-PCIE-32GB\nGPU 3: Tesla V100-PCIE-32GB\n\nNvidia driver version: 535.183.01\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\nAddress sizes: 43 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 32\nOn-line CPU(s) list: 0-31\nVendor ID: AuthenticAMD\nModel name: AMD Ryzen Threadripper 1950X 16-Core Processor\nCPU family: 23\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 16\nSocket(s): 1\nStepping: 1\nFrequency boost: enabled\nCPU(s) scaling MHz: 66%\nCPU max MHz: 3400.0000\nCPU min MHz: 2200.0000\nBogoMIPS: 6786.43\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 amd_dcm aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sev\nVirtualization: AMD-V\nL1d cache: 512 KiB (16 instances)\nL1i cache: 1 MiB (16 instances)\nL2 cache: 8 MiB (16 instances)\nL3 cache: 32 MiB (4 instances)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-31\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 Reg file data sampling: Not affected\nVulnerability Retbleed: Mitigation; untrained return thunk; SMT vulnerable\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; 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==2.1.3\n[pip3] torch==2.5.1\n[pip3] triton==3.1.0\n[conda] numpy 2.1.3 pypi_0 pypi\n[conda] torch 2.5.1 pypi_0 pypi\n[conda] triton 3.1.0 pypi_0 pypi",
"transformers_version": "4.46.3",
"upper_git_hash": null,
"tokenizer_pad_token": [
"<|finetune_right_pad_id|>",
"128004"
],
"tokenizer_eos_token": [
"<|eot_id|>",
"128009"
],
"tokenizer_bos_token": [
"<|begin_of_text|>",
"128000"
],
"eot_token_id": 128009,
"max_length": 131072,
"task_hashes": {
"leaderboard_musr_murder_mysteries": "a696259562ea5c5c09a2613e30526fae1de29f55da9e28e8d7e8a53027e6d330",
"leaderboard_musr_object_placements": "3aa8c5e5bc59cd6ba2326269b9f0bf3cee8cba1b4e9e1d1330cf5f1f59ea0dce",
"leaderboard_musr_team_allocation": "5a75f135c145ee861a1cf31b63346709ef41b9d542be6a61c5818c210a3797a5"
},
"model_source": "hf",
"model_name": "DevQuasar/analytical_reasoning_r16a32_unsloth-Llama-3.2-3B-Instruct-bnb-4bit",
"model_name_sanitized": "DevQuasar__analytical_reasoning_r16a32_unsloth-Llama-3.2-3B-Instruct-bnb-4bit",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": null,
"chat_template_sha": null,
"start_time": 52195.45405349,
"end_time": 52407.302247922,
"total_evaluation_time_seconds": "211.84819443200104"
} |