{
"results": {
"aime24_nofigures": {
"alias": "aime24_nofigures",
"exact_match,none": 0.5666666666666667,
"exact_match_stderr,none": "N/A",
"extracted_answers,none": -1,
"extracted_answers_stderr,none": "N/A"
},
"aime25_nofigures": {
"alias": "aime25_nofigures",
"exact_match,none": 0.4666666666666667,
"exact_match_stderr,none": "N/A",
"extracted_answers,none": -1,
"extracted_answers_stderr,none": "N/A"
}
},
"group_subtasks": {
"aime24_nofigures": [],
"aime25_nofigures": []
},
"configs": {
"aime24_nofigures": {
"task": "aime24_nofigures",
"tag": [
"math_word_problems"
],
"dataset_path": "simplescaling/aime24_nofigures",
"dataset_name": "default",
"test_split": "train",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n solution = doc.get(\"solution\", doc.get(\"orig_solution\", doc.get(\"orig_orig_solution\")))\n problem = doc.get(\"problem\", doc.get(\"question\"))\n answer = doc.get(\"answer\", doc.get(\"orig_answer\", doc.get(\"orig_orig_answer\")))\n if solution is None:\n print(\"Warning: No solution found; DOC:\", doc)\n out_doc = {\n \"problem\": problem,\n \"solution\": solution,\n \"answer\": answer,\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n return dataset.map(_process_doc)\n",
"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return QUERY_TEMPLATE.format(Question=doc.get(\"problem\", doc.get(\"question\")))\n",
"doc_to_target": "answer",
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # bp()\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n }\n\n if os.getenv(\"PROCESSOR\", \"\") == \"gpt-4o-mini\":\n sampler = ChatCompletionSampler(model=\"gpt-4o-mini\")\n else:\n print(f\"Unknown processor: {os.getenv('PROCESSOR')}; set 'PROCESSOR=gpt-4o-mini' and 'OPENAI_API_KEY=YOUR_KEY' for best results.\")\n sampler = None\n\n if isinstance(doc[\"answer\"], str) and doc[\"answer\"].isdigit():\n gt = str(int(doc[\"answer\"])) # 023 -> 23\n else:\n gt = str(doc[\"answer\"])\n split_tokens = [\"<|im_start|>answer\\n\", \"<|im_start|>\"]\n\n for i, a in enumerate(results, start=1):\n if split_tokens[0] in a:\n a = a.split(split_tokens[0])[-1]\n elif split_tokens[1] in a:\n a = a.split(split_tokens[1])[-1]\n if \"\\n\" in a:\n a = \"\\n\".join(a.split(\"\\n\")[1:])\n\n if (box := last_boxed_only_string(a)) is not None:\n a = remove_boxed(box)\n # re.DOTALL is key such that newlines are included e.g. if it does `Answer: Here is the solution:\\n\\n10`\n elif (matches := re.findall(ANSWER_PATTERN, a, re.DOTALL)) != []:\n a = matches[-1] # Get the last match\n\n # AIME answers are from 000 to 999 so often it is a digit anyways\n if (a.isdigit()) and (gt.isdigit()):\n a = str(int(a)) # 023 -> 23\n elif sampler is not None:\n options = [gt] + list(set(metrics[\"extracted_answers\"]) - {gt})\n if len(options) > 7:\n # Could switch back to exact returning like in AIME in that case\n # Problem with exact returning is that it sometimes messes up small things like a dollar sign\n print(\"Warning: Lots of options which may harm indexing performance:\", options) \n # This ensures that if doc['answer'] is \\text{Evelyn} it is represented as such and not \\\\text{Evelyn}\n options_str = \"[\" + \", \".join([\"'\" + str(o) + \"'\" for o in options]) + \"]\"\n # a = extract_answer(sampler, options, a)\n idx = extract_answer_idx(sampler, options_str, a)\n if idx != \"-1\":\n if idx.isdigit():\n idx = int(idx) - 1\n if len(options) > idx >= 0:\n a = options[idx]\n else:\n print(\"Warning: Index out of bounds; leaving answer unchanged\\n\", a, \"\\noptions\", options_str, \"\\ndoc['answer']\", gt, \"\\nidx\", idx)\n else:\n print(\"Warning: Processing did not produce integer index\\na\", a, \"\\noptions\", options_str, \"\\ndoc['answer']\", gt, \"\\nidx\", idx)\n else:\n pass # TODO: Maybe add back legacy processing\n\n metrics[\"extracted_answers\"].append(a)\n a = int(a == gt)\n if not(a): # Optional logging\n print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + gt)\n if i == 1:\n metrics[\"exact_match\"] = a\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(a)\n elif i > 1:\n metrics[\"exact_matches\"].append(a)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(gt == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n\n return metrics\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true
},
{
"metric": "extracted_answers",
"aggregation": "bypass",
"higher_is_better": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [],
"do_sample": false,
"temperature": 0.0,
"max_gen_toks": 32768,
"max_tokens_thinking": "auto",
"thinking_n_ignore": 2,
"thinking_n_ignore_str": "Wait ..."
},
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"aime25_nofigures": {
"task": "aime25_nofigures",
"tag": [
"math_word_problems"
],
"dataset_path": "TIGER-Lab/AIME25",
"dataset_name": "default",
"test_split": "train",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc: dict) -> dict:\n solution = doc.get(\"solution\", doc.get(\"orig_solution\", doc.get(\"orig_orig_solution\")))\n problem = doc.get(\"problem\", doc.get(\"question\"))\n answer = doc.get(\"answer\", doc.get(\"orig_answer\", doc.get(\"orig_orig_answer\")))\n if solution is None:\n print(\"Warning: No solution found; DOC:\", doc)\n out_doc = {\n \"problem\": problem,\n \"solution\": solution,\n \"answer\": answer,\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n return dataset.map(_process_doc)\n",
"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return QUERY_TEMPLATE.format(Question=doc.get(\"problem\", doc.get(\"question\")))\n",
"doc_to_target": "answer",
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n metrics = {\"exact_match\": None, \"extracted_answers\": []}\n # bp()\n # Multiple results -> we are measuring cov/maj etc\n if isinstance(results[0], list):\n results = results[0]\n n_res = len(results) # e.g. 64\n n_res_list = [2**i for i in range(1, int(n_res.bit_length()))] # e.g. [2, 4, 8, 16, 32, 64]\n metrics = {\n **metrics,\n \"exact_matches\": [],\n **{f\"cov@{n}\": -1 for n in n_res_list},\n **{f\"maj@{n}\": -1 for n in n_res_list},\n }\n\n if os.getenv(\"PROCESSOR\", \"\") == \"gpt-4o-mini\":\n sampler = ChatCompletionSampler(model=\"gpt-4o-mini\")\n else:\n print(f\"Unknown processor: {os.getenv('PROCESSOR')}; set 'PROCESSOR=gpt-4o-mini' and 'OPENAI_API_KEY=YOUR_KEY' for best results.\")\n sampler = None\n\n if isinstance(doc[\"answer\"], str) and doc[\"answer\"].isdigit():\n gt = str(int(doc[\"answer\"])) # 023 -> 23\n else:\n gt = str(doc[\"answer\"])\n split_tokens = [\"<|im_start|>answer\\n\", \"<|im_start|>\"]\n\n for i, a in enumerate(results, start=1):\n if split_tokens[0] in a:\n a = a.split(split_tokens[0])[-1]\n elif split_tokens[1] in a:\n a = a.split(split_tokens[1])[-1]\n if \"\\n\" in a:\n a = \"\\n\".join(a.split(\"\\n\")[1:])\n\n if (box := last_boxed_only_string(a)) is not None:\n a = remove_boxed(box)\n # re.DOTALL is key such that newlines are included e.g. if it does `Answer: Here is the solution:\\n\\n10`\n elif (matches := re.findall(ANSWER_PATTERN, a, re.DOTALL)) != []:\n a = matches[-1] # Get the last match\n\n # AIME answers are from 000 to 999 so often it is a digit anyways\n if (a.isdigit()) and (gt.isdigit()):\n a = str(int(a)) # 023 -> 23\n elif sampler is not None:\n options = [gt] + list(set(metrics[\"extracted_answers\"]) - {gt})\n if len(options) > 7:\n # Could switch back to exact returning like in AIME in that case\n # Problem with exact returning is that it sometimes messes up small things like a dollar sign\n print(\"Warning: Lots of options which may harm indexing performance:\", options) \n # This ensures that if doc['answer'] is \\text{Evelyn} it is represented as such and not \\\\text{Evelyn}\n options_str = \"[\" + \", \".join([\"'\" + str(o) + \"'\" for o in options]) + \"]\"\n # a = extract_answer(sampler, options, a)\n idx = extract_answer_idx(sampler, options_str, a)\n if idx != \"-1\":\n if idx.isdigit():\n idx = int(idx) - 1\n if len(options) > idx >= 0:\n a = options[idx]\n else:\n print(\"Warning: Index out of bounds; leaving answer unchanged\\n\", a, \"\\noptions\", options_str, \"\\ndoc['answer']\", gt, \"\\nidx\", idx)\n else:\n print(\"Warning: Processing did not produce integer index\\na\", a, \"\\noptions\", options_str, \"\\ndoc['answer']\", gt, \"\\nidx\", idx)\n else:\n pass # TODO: Maybe add back legacy processing\n\n metrics[\"extracted_answers\"].append(a)\n a = int(a == gt)\n if not(a): # Optional logging\n print(\"Marked incorrect\\na \" + metrics[\"extracted_answers\"][-1] + \"\\ndoc['answer'] \" + gt)\n if i == 1:\n metrics[\"exact_match\"] = a\n if \"exact_matches\" in metrics:\n metrics[\"exact_matches\"].append(a)\n elif i > 1:\n metrics[\"exact_matches\"].append(a)\n if i in n_res_list:\n metrics[f\"cov@{i}\"] = int(1 in metrics[\"exact_matches\"])\n metrics[f\"maj@{i}\"] = int(gt == Counter(metrics[\"extracted_answers\"]).most_common(1)[0][0])\n\n return metrics\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true
},
{
"metric": "extracted_answers",
"aggregation": "bypass",
"higher_is_better": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [],
"do_sample": false,
"temperature": 0.0,
"max_gen_toks": 32768,
"max_tokens_thinking": "auto",
"thinking_n_ignore": 2,
"thinking_n_ignore_str": "Wait ..."
},
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
}
},
"versions": {
"aime24_nofigures": 1.0,
"aime25_nofigures": 1.0
},
"n-shot": {
"aime24_nofigures": 0,
"aime25_nofigures": 0
},
"higher_is_better": {
"aime24_nofigures": {
"exact_match": true,
"extracted_answers": true
},
"aime25_nofigures": {
"exact_match": true,
"extracted_answers": true
}
},
"n-samples": {
"aime25_nofigures": {
"original": 15,
"effective": 15
},
"aime24_nofigures": {
"original": 30,
"effective": 30
}
},
"config": {
"model": "vllm",
"model_args": "pretrained=simplescaling/s1.1-32B,dtype=float32,tensor_parallel_size=8",
"batch_size": "auto",
"batch_sizes": [],
"device": null,
"use_cache": null,
"limit": null,
"bootstrap_iters": 0,
"gen_kwargs": {
"max_gen_toks": 32768,
"max_tokens_thinking": "auto",
"thinking_n_ignore": 2,
"thinking_n_ignore_str": "Wait ..."
},
"random_seed": 0,
"numpy_seed": 1234,
"torch_seed": 1234,
"fewshot_seed": 1234
},
"git_hash": "a465d7f",
"date": 1739564269.3372107,
"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: Ubuntu 22.04.5 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.35\n\nPython version: 3.11.10 (main, Sep 7 2024, 18:35:41) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-6.8.0-50-generic-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 L40\nGPU 1: NVIDIA L40\nGPU 2: NVIDIA L40\nGPU 3: NVIDIA L40\nGPU 4: NVIDIA L40\nGPU 5: NVIDIA L40\nGPU 6: NVIDIA L40\nGPU 7: NVIDIA L40\n\nNvidia driver version: 565.57.01\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: 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: 3799.0720\nCPU min MHz: 1500.0000\nBogoMIPS: 6500.01\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 amd_lbr_v2 nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 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 perfmon_v2 ibrs ibpb stibp ibrs_enhanced 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 user_shstk avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d debug_swap\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 Reg file data sampling: 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; Enhanced / Automatic IBRS; IBPB conditional; STIBP always-on; 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==1.26.3\n[pip3] torch==2.5.1\n[pip3] torchaudio==2.5.1\n[pip3] torchvision==0.20.1\n[pip3] triton==3.1.0\n[conda] Could not collect",
"transformers_version": "4.48.3",
"upper_git_hash": null,
"tokenizer_pad_token": [
"<|endoftext|>",
"151643"
],
"tokenizer_eos_token": [
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"151645"
],
"tokenizer_bos_token": [
null,
"None"
],
"eot_token_id": 151645,
"max_length": 32768,
"task_hashes": {
"aime25_nofigures": "62c256d557633e4054a8cd84e22eb1971f82e7c4cee36cb508cf37362cf7f67a",
"aime24_nofigures": "3eb5fb976b3f4dea4e4e2a2caf5efa2cfea98aa3ae68cd0f3bfa8a3f197b0e2d"
},
"model_source": "vllm",
"model_name": "simplescaling/s1.1-32B",
"model_name_sanitized": "simplescaling__s1.1-32B",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within XML tags:\\n\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n\\n\\nFor each function call, return a json object with function name and arguments within XML tags:\\n\\n{\\\"name\\\": , \\\"arguments\\\": }\\n<|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n\\n' }}\n {{- message.content }}\n {{- '\\n' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
"chat_template_sha": "cd8e9439f0570856fd70470bf8889ebd8b5d1107207f67a5efb46e342330527f",
"start_time": 3411381.936710586,
"end_time": 3415139.434631916,
"total_evaluation_time_seconds": "3757.4979213303886"
}