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Model: lanawwas/ALLaM-7B-Instruct-preview
Source: Original Platform
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ModelHub XC
2026-04-22 10:54:04 +08:00
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evaluations/en/Qwen2.5-7B-Instruct/agieval_0_shot.json Normal file
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evaluations/en/Qwen2.5-7B-Instruct/arc_challenge_0_shot.json Normal file
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{
"results": {
"arc_challenge": {
"alias": "arc_challenge",
"acc,none": 0.492320819112628,
"acc_stderr,none": 0.01460966744089257,
"acc_norm,none": 0.5127986348122867,
"acc_norm_stderr,none": 0.014606603181012538
}
},
"group_subtasks": {
"arc_challenge": []
},
"configs": {
"arc_challenge": {
"task": "arc_challenge",
"tag": [
"ai2_arc"
],
"dataset_path": "allenai/ai2_arc",
"dataset_name": "ARC-Challenge",
"training_split": "train",
"validation_split": "validation",
"test_split": "test",
"doc_to_text": "Question: {{question}}\nAnswer:",
"doc_to_target": "{{choices.label.index(answerKey)}}",
"doc_to_choice": "{{choices.text}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
},
{
"metric": "acc_norm",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": true,
"doc_to_decontamination_query": "Question: {{question}}\nAnswer:",
"metadata": {
"version": 1.0
}
}
},
"versions": {
"arc_challenge": 1.0
},
"n-shot": {
"arc_challenge": 0
},
"higher_is_better": {
"arc_challenge": {
"acc": true,
"acc_norm": true
}
},
"n-samples": {
"arc_challenge": {
"original": 1172,
"effective": 1172
}
},
"config": {
"model": "hf",
"model_args": "parallelize=True,pretrained=Qwen/Qwen2.5-7B-Instruct,trust_remote_code=True,mm=False,trust_remote_code=True",
"model_num_parameters": 7615616512,
"model_dtype": "torch.bfloat16",
"model_revision": "main",
"model_sha": "bb46c15ee4bb56c5b63245ef50fd7637234d6f75",
"batch_size": 1,
"batch_sizes": [],
"device": null,
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"fewshot_seed": 1234
},
"git_hash": "3127d82f",
"date": 1732457569.0900333,
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"transformers_version": "4.46.3",
"upper_git_hash": null,
"tokenizer_pad_token": [
"<|endoftext|>",
"151643"
],
"tokenizer_eos_token": [
"<|im_end|>",
"151645"
],
"tokenizer_bos_token": [
null,
"None"
],
"eot_token_id": 151645,
"max_length": 32768,
"task_hashes": {},
"model_source": "hf",
"model_name": "Qwen/Qwen2.5-7B-Instruct",
"model_name_sanitized": "Qwen__Qwen2.5-7B-Instruct",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": null,
"chat_template_sha": null,
"start_time": 937908.10005559,
"end_time": 938434.455070034,
"total_evaluation_time_seconds": "526.3550144439796"
}

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evaluations/en/Qwen2.5-7B-Instruct/gpqa_main_n_shot_0_shot.json Normal file
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{
"results": {
"gpqa_main_n_shot": {
"alias": "gpqa_main_n_shot",
"acc,none": 0.265625,
"acc_stderr,none": 0.02089005840079951,
"acc_norm,none": 0.265625,
"acc_norm_stderr,none": 0.02089005840079951
}
},
"group_subtasks": {
"gpqa_main_n_shot": []
},
"configs": {
"gpqa_main_n_shot": {
"task": "gpqa_main_n_shot",
"tag": "gpqa",
"dataset_path": "Idavidrein/gpqa",
"dataset_name": "gpqa_main",
"training_split": "train",
"validation_split": "train",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n choices = [\n preprocess(doc[\"Incorrect Answer 1\"]),\n preprocess(doc[\"Incorrect Answer 2\"]),\n preprocess(doc[\"Incorrect Answer 3\"]),\n preprocess(doc[\"Correct Answer\"]),\n ]\n\n rng.shuffle(choices)\n correct_answer_index = choices.index(preprocess(doc[\"Correct Answer\"]))\n\n out_doc = {\n \"choice1\": choices[0],\n \"choice2\": choices[1],\n \"choice3\": choices[2],\n \"choice4\": choices[3],\n \"answer\": f\"({chr(65 + correct_answer_index)})\",\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
"doc_to_text": "Question: {{Question}}\nChoices:\n(A) {{choice1}}\n(B) {{choice2}}\n(C) {{choice3}}\n(D) {{choice4}}\nAnswer:",
"doc_to_target": "answer",
"doc_to_choice": [
"(A)",
"(B)",
"(C)",
"(D)"
],
"description": "Here are some example questions from experts. Answer the final question yourself, following the format of the previous questions exactly.\n",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc",
"aggregation": "mean",
"higher_is_better": true
},
{
"metric": "acc_norm",
"aggregation": "mean",
"higher_is_better": true
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 2.0
}
}
},
"versions": {
"gpqa_main_n_shot": 2.0
},
"n-shot": {
"gpqa_main_n_shot": 0
},
"higher_is_better": {
"gpqa_main_n_shot": {
"acc": true,
"acc_norm": true
}
},
"n-samples": {
"gpqa_main_n_shot": {
"original": 448,
"effective": 448
}
},
"config": {
"model": "hf",
"model_args": "parallelize=True,pretrained=Qwen/Qwen2.5-7B-Instruct,trust_remote_code=True,mm=False,trust_remote_code=True",
"model_num_parameters": 7615616512,
"model_dtype": "torch.bfloat16",
"model_revision": "main",
"model_sha": "bb46c15ee4bb56c5b63245ef50fd7637234d6f75",
"batch_size": 1,
"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": "3127d82f",
"date": 1732102628.9472814,
"pretty_env_info": "PyTorch version: 2.1.0a0+29c30b1\nIs debug build: False\nCUDA used to build PyTorch: 12.2\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 3.27.1\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-5.15.0-1064-azure-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.2.128\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA A100 80GB PCIe\nGPU 1: NVIDIA A100 80GB PCIe\n\nNvidia driver version: 535.161.08\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.4\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.4\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: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 48\nOn-line CPU(s) list: 0-47\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V13 64-Core Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 1\nStepping: 1\nBogoMIPS: 4890.87\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 tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr rdpru arat umip vaes vpclmulqdq rdpid fsrm\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 1.5 MiB (48 instances)\nL1i cache: 1.5 MiB (48 instances)\nL2 cache: 24 MiB (48 instances)\nL3 cache: 192 MiB (6 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\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, no microcode\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; 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==1.22.2\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.1.0a0+29c30b1\n[pip3] torch-tensorrt==2.0.0.dev0\n[pip3] torchaudio==2.1.0\n[pip3] torchdata==0.7.0a0\n[pip3] torchmetrics==1.2.0\n[pip3] torchvision==0.16.0a0\n[pip3] triton==2.0.0.dev20221202\n[conda] Could not collect",
"transformers_version": "4.46.3",
"upper_git_hash": null,
"tokenizer_pad_token": [
"<|endoftext|>",
"151643"
],
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"None"
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"eot_token_id": 151645,
"max_length": 32768,
"task_hashes": {},
"model_source": "hf",
"model_name": "Qwen/Qwen2.5-7B-Instruct",
"model_name_sanitized": "Qwen__Qwen2.5-7B-Instruct",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": null,
"chat_template_sha": null,
"start_time": 14411.236871797,
"end_time": 14860.036437357,
"total_evaluation_time_seconds": "448.7995655599989"
}

157
evaluations/en/Qwen2.5-7B-Instruct/gsm8k_5_shot.json Normal file
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@@ -0,0 +1,157 @@
{
"results": {
"gsm8k": {
"alias": "gsm8k",
"exact_match,strict-match": 0.43290371493555724,
"exact_match_stderr,strict-match": 0.013647916362576052,
"exact_match,flexible-extract": 0.4382107657316149,
"exact_match_stderr,flexible-extract": 0.013666915917255072
}
},
"group_subtasks": {
"gsm8k": []
},
"configs": {
"gsm8k": {
"task": "gsm8k",
"tag": [
"math_word_problems"
],
"dataset_path": "gsm8k",
"dataset_name": "main",
"training_split": "train",
"test_split": "test",
"fewshot_split": "train",
"doc_to_text": "Question: {{question}}\nAnswer:",
"doc_to_target": "{{answer}}",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 5,
"metric_list": [
{
"metric": "exact_match",
"aggregation": "mean",
"higher_is_better": true,
"ignore_case": true,
"ignore_punctuation": false,
"regexes_to_ignore": [
",",
"\\$",
"(?s).*#### ",
"\\.$"
]
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [
"Question:",
"</s>",
"<|im_end|>"
],
"do_sample": false,
"temperature": 0.0
},
"repeats": 1,
"filter_list": [
{
"name": "strict-match",
"filter": [
{
"function": "regex",
"regex_pattern": "#### (\\-?[0-9\\.\\,]+)"
},
{
"function": "take_first"
}
]
},
{
"name": "flexible-extract",
"filter": [
{
"function": "regex",
"group_select": -1,
"regex_pattern": "(-?[$0-9.,]{2,})|(-?[0-9]+)"
},
{
"function": "take_first"
}
]
}
],
"should_decontaminate": false,
"metadata": {
"version": 3.0
}
}
},
"versions": {
"gsm8k": 3.0
},
"n-shot": {
"gsm8k": 5
},
"higher_is_better": {
"gsm8k": {
"exact_match": true
}
},
"n-samples": {
"gsm8k": {
"original": 1319,
"effective": 1319
}
},
"config": {
"model": "hf",
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"model_num_parameters": 7615616512,
"model_dtype": "torch.bfloat16",
"model_revision": "main",
"model_sha": "bb46c15ee4bb56c5b63245ef50fd7637234d6f75",
"batch_size": 1,
"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": "3127d82f",
"date": 1732457548.7457082,
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122
evaluations/en/Qwen2.5-7B-Instruct/hellaswag_0_shot.json Normal file
View File

@@ -0,0 +1,122 @@
{
"results": {
"hellaswag": {
"alias": "hellaswag",
"acc,none": 0.6094403505277833,
"acc_stderr,none": 0.004868787333436608,
"acc_norm,none": 0.7954590718980283,
"acc_norm_stderr,none": 0.004025413948619421
}
},
"group_subtasks": {
"hellaswag": []
},
"configs": {
"hellaswag": {
"task": "hellaswag",
"tag": [
"multiple_choice"
],
"dataset_path": "hellaswag",
"dataset_kwargs": {
"trust_remote_code": true
},
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"validation_split": "validation",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n out_doc = {\n \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n \"gold\": int(doc[\"label\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n",
"doc_to_text": "{{query}}",
"doc_to_target": "{{label}}",
"doc_to_choice": "choices",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
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"metric_list": [
{
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"aggregation": "mean",
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},
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"metric": "acc_norm",
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"higher_is_better": true
}
],
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"should_decontaminate": false,
"metadata": {
"version": 1.0
}
}
},
"versions": {
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},
"n-shot": {
"hellaswag": 0
},
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"acc": true,
"acc_norm": true
}
},
"n-samples": {
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"effective": 10042
}
},
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"tokenizer_pad_token": [
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"system_instruction": null,
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"chat_template_sha": null,
"start_time": 939985.69301667,
"end_time": 941989.181357355,
"total_evaluation_time_seconds": "2003.4883406850277"
}

313
evaluations/en/Qwen2.5-7B-Instruct/hendrycks_ethics_0_shot.json Normal file
View File

@@ -0,0 +1,313 @@
{
"results": {
"ethics_cm": {
"alias": "ethics_cm",
"acc,none": 0.7341055341055341,
"acc_stderr,none": 0.00708915198928491
},
"ethics_deontology": {
"alias": "ethics_deontology",
"acc,none": 0.6223581757508343,
"acc_stderr,none": 0.00808557287309968
},
"ethics_justice": {
"alias": "ethics_justice",
"acc,none": 0.7795857988165681,
"acc_stderr,none": 0.007973127756580458
},
"ethics_utilitarianism": {
"alias": "ethics_utilitarianism",
"acc,none": 0.6378951747088186,
"acc_stderr,none": 0.006931939337695583
},
"ethics_virtue": {
"alias": "ethics_virtue",
"acc,none": 0.9139698492462311,
"acc_stderr,none": 0.003975926854665248
}
},
"group_subtasks": {
"ethics_justice": [],
"ethics_virtue": [],
"ethics_utilitarianism": [],
"ethics_deontology": [],
"ethics_cm": []
},
"configs": {
"ethics_cm": {
"task": "ethics_cm",
"tag": [
"hendrycks_ethics"
],
"dataset_path": "EleutherAI/hendrycks_ethics",
"dataset_name": "commonsense",
"dataset_kwargs": {
"trust_remote_code": true
},
"training_split": "train",
"test_split": "test",
"doc_to_text": "{{input}}\nQuestion: Is this wrong?\nAnswer:",
"doc_to_target": "label",
"doc_to_choice": [
"no",
"yes"
],
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc"
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"ethics_deontology": {
"task": "ethics_deontology",
"tag": [
"hendrycks_ethics"
],
"dataset_path": "EleutherAI/hendrycks_ethics",
"dataset_name": "deontology",
"dataset_kwargs": {
"trust_remote_code": true
},
"training_split": "train",
"test_split": "test",
"doc_to_text": "Question: Would most people believe this reasonable or unreasonable to say? \"{{scenario}} {{excuse.rstrip()}}\"\nAnswer:",
"doc_to_target": "label",
"doc_to_choice": [
"unreasonable",
"reasonable"
],
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc"
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"ethics_justice": {
"task": "ethics_justice",
"tag": [
"hendrycks_ethics"
],
"dataset_path": "EleutherAI/hendrycks_ethics",
"dataset_name": "justice",
"dataset_kwargs": {
"trust_remote_code": true
},
"training_split": "train",
"test_split": "test",
"doc_to_text": "Question: Would most people believe this reasonable or unreasonable to say? \"{{scenario}}\"\nAnswer:",
"doc_to_target": "label",
"doc_to_choice": [
"unreasonable",
"reasonable"
],
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc"
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"ethics_utilitarianism": {
"task": "ethics_utilitarianism",
"tag": [
"hendrycks_ethics"
],
"dataset_path": "EleutherAI/hendrycks_ethics",
"dataset_name": "utilitarianism",
"dataset_kwargs": {
"trust_remote_code": true
},
"training_split": "train",
"test_split": "test",
"doc_to_text": "def doc_to_text(doc) -> str:\n doc = _preproc_doc(doc)\n return f\"Scenario 1: {doc['scenarios'][0]}\\nScenario 2: {doc['scenarios'][1]}\\nQuestion: Is Scenario 1 preferable?\\nAnswer:\"\n",
"doc_to_target": "def doc_to_target(doc):\n doc = _preproc_doc(doc)\n return doc[\"label\"]\n",
"doc_to_choice": [
"no",
"yes"
],
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc"
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
},
"ethics_virtue": {
"task": "ethics_virtue",
"tag": [
"hendrycks_ethics"
],
"dataset_path": "EleutherAI/hendrycks_ethics",
"dataset_name": "virtue",
"dataset_kwargs": {
"trust_remote_code": true
},
"training_split": "train",
"test_split": "test",
"doc_to_text": "Sentence: {{scenario}}\nQuestion: Does the character in this sentence exhibit the trait \"{{trait}}\"?\nAnswer:",
"doc_to_target": "label",
"doc_to_choice": [
"no",
"yes"
],
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "acc"
}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 1.0
}
}
},
"versions": {
"ethics_cm": 1.0,
"ethics_deontology": 1.0,
"ethics_justice": 1.0,
"ethics_utilitarianism": 1.0,
"ethics_virtue": 1.0
},
"n-shot": {
"ethics_cm": 0,
"ethics_deontology": 0,
"ethics_justice": 0,
"ethics_utilitarianism": 0,
"ethics_virtue": 0
},
"higher_is_better": {
"ethics_cm": {
"acc": true
},
"ethics_deontology": {
"acc": true
},
"ethics_justice": {
"acc": true
},
"ethics_utilitarianism": {
"acc": true
},
"ethics_virtue": {
"acc": true
}
},
"n-samples": {
"ethics_cm": {
"original": 3885,
"effective": 3885
},
"ethics_deontology": {
"original": 3596,
"effective": 3596
},
"ethics_utilitarianism": {
"original": 4808,
"effective": 4808
},
"ethics_virtue": {
"original": 4975,
"effective": 4975
},
"ethics_justice": {
"original": 2704,
"effective": 2704
}
},
"config": {
"model": "hf",
"model_args": "pretrained=Qwen/Qwen2.5-7B-Instruct,trust_remote_code=True,cache_dir=/tmp,parallelize=False",
"model_num_parameters": 7615616512,
"model_dtype": "torch.bfloat16",
"model_revision": "main",
"model_sha": "a09a35458c702b33eeacc393d103063234e8bc28",
"batch_size": "auto",
"batch_sizes": [
32
],
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"limit": null,
"bootstrap_iters": 100000,
"gen_kwargs": null,
"random_seed": 0,
"numpy_seed": 1234,
"torch_seed": 1234,
"fewshot_seed": 1234
},
"git_hash": "788a3672",
"date": 1737762182.1632717,
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"transformers_version": "4.48.1",
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"model_name_sanitized": "Qwen__Qwen2.5-7B-Instruct",
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"chat_template": null,
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}

136
evaluations/en/Qwen2.5-7B-Instruct/ifeval_0_shot.json Normal file
View File

@@ -0,0 +1,136 @@
{
"results": {
"ifeval": {
"alias": "ifeval",
"prompt_level_strict_acc,none": 0.5730129390018485,
"prompt_level_strict_acc_stderr,none": 0.02128593305006131,
"inst_level_strict_acc,none": 0.6822541966426858,
"inst_level_strict_acc_stderr,none": "N/A",
"prompt_level_loose_acc,none": 0.6395563770794824,
"prompt_level_loose_acc_stderr,none": 0.020661469669879428,
"inst_level_loose_acc,none": 0.7326139088729017,
"inst_level_loose_acc_stderr,none": "N/A"
}
},
"group_subtasks": {
"ifeval": []
},
"configs": {
"ifeval": {
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"dataset_path": "google/IFEval",
"test_split": "train",
"doc_to_text": "prompt",
"doc_to_target": 0,
"process_results": "def process_results(doc, results):\n inp = InputExample(\n key=doc[\"key\"],\n instruction_id_list=doc[\"instruction_id_list\"],\n prompt=doc[\"prompt\"],\n kwargs=doc[\"kwargs\"],\n )\n response = results[0]\n\n out_strict = test_instruction_following_strict(inp, response)\n out_loose = test_instruction_following_loose(inp, response)\n\n return {\n \"prompt_level_strict_acc\": out_strict.follow_all_instructions,\n \"inst_level_strict_acc\": out_strict.follow_instruction_list,\n \"prompt_level_loose_acc\": out_loose.follow_all_instructions,\n \"inst_level_loose_acc\": out_loose.follow_instruction_list,\n }\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
"metric": "prompt_level_strict_acc",
"aggregation": "mean",
"higher_is_better": true
},
{
"metric": "inst_level_strict_acc",
"aggregation": "def agg_inst_level_acc(items):\n flat_items = [item for sublist in items for item in sublist]\n inst_level_acc = sum(flat_items) / len(flat_items)\n return inst_level_acc\n",
"higher_is_better": true
},
{
"metric": "prompt_level_loose_acc",
"aggregation": "mean",
"higher_is_better": true
},
{
"metric": "inst_level_loose_acc",
"aggregation": "def agg_inst_level_acc(items):\n flat_items = [item for sublist in items for item in sublist]\n inst_level_acc = sum(flat_items) / len(flat_items)\n return inst_level_acc\n",
"higher_is_better": true
}
],
"output_type": "generate_until",
"generation_kwargs": {
"until": [],
"do_sample": false,
"temperature": 0.0,
"max_gen_toks": 1280
},
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 4.0
}
}
},
"versions": {
"ifeval": 4.0
},
"n-shot": {
"ifeval": 0
},
"higher_is_better": {
"ifeval": {
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"inst_level_strict_acc": true,
"prompt_level_loose_acc": true,
"inst_level_loose_acc": true
}
},
"n-samples": {
"ifeval": {
"original": 541,
"effective": 541
}
},
"config": {
"model": "hf",
"model_args": "pretrained=Qwen/Qwen2.5-7B-Instruct,trust_remote_code=True,cache_dir=/tmp,parallelize=False",
"model_num_parameters": 7615616512,
"model_dtype": "torch.bfloat16",
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"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": "788a3672",
"date": 1737787208.1303658,
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"transformers_version": "4.48.1",
"upper_git_hash": "086919bd66f4e15fdcd4b792a7b27a698c1ba091",
"tokenizer_pad_token": [
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"151643"
],
"tokenizer_eos_token": [
"<|im_end|>",
"151645"
],
"tokenizer_bos_token": [
null,
"None"
],
"eot_token_id": 151645,
"max_length": 32768,
"task_hashes": {},
"model_source": "hf",
"model_name": "Qwen/Qwen2.5-7B-Instruct",
"model_name_sanitized": "Qwen__Qwen2.5-7B-Instruct",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": null,
"chat_template_sha": null,
"start_time": 34079.895170834,
"end_time": 38297.123696959,
"total_evaluation_time_seconds": "4217.228526125"
}

525
evaluations/en/Qwen2.5-7B-Instruct/minerva_math_4_shot.json Normal file
View File

@@ -0,0 +1,525 @@
{
"results": {
"minerva_math": {
"exact_match,none": 0.1204,
"exact_match_stderr,none": 0.004557251536754508,
"alias": "minerva_math"
},
"minerva_math_algebra": {
"alias": " - minerva_math_algebra",
"exact_match,none": 0.17101937657961247,
"exact_match_stderr,none": 0.010933331211377626
},
"minerva_math_counting_and_prob": {
"alias": " - minerva_math_counting_and_prob",
"exact_match,none": 0.14135021097046413,
"exact_match_stderr,none": 0.016018641943125127
},
"minerva_math_geometry": {
"alias": " - minerva_math_geometry",
"exact_match,none": 0.07306889352818371,
"exact_match_stderr,none": 0.011903537529007871
},
"minerva_math_intermediate_algebra": {
"alias": " - minerva_math_intermediate_algebra",
"exact_match,none": 0.07198228128460686,
"exact_match_stderr,none": 0.008605729055597196
},
"minerva_math_num_theory": {
"alias": " - minerva_math_num_theory",
"exact_match,none": 0.08333333333333333,
"exact_match_stderr,none": 0.011904761904761852
},
"minerva_math_prealgebra": {
"alias": " - minerva_math_prealgebra",
"exact_match,none": 0.17221584385763491,
"exact_match_stderr,none": 0.012800751907784132
},
"minerva_math_precalc": {
"alias": " - minerva_math_precalc",
"exact_match,none": 0.06776556776556776,
"exact_match_stderr,none": 0.010766359056008468
}
},
"groups": {
"minerva_math": {
"exact_match,none": 0.1204,
"exact_match_stderr,none": 0.004557251536754508,
"alias": "minerva_math"
}
},
"group_subtasks": {
"minerva_math": [
"minerva_math_algebra",
"minerva_math_counting_and_prob",
"minerva_math_geometry",
"minerva_math_intermediate_algebra",
"minerva_math_num_theory",
"minerva_math_prealgebra",
"minerva_math_precalc"
]
},
"configs": {
"minerva_math_algebra": {
"task": "minerva_math_algebra",
"tag": [
"math_word_problems"
],
"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\": normalize_final_answer(\n remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n ),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n return dataset.map(_process_doc)\n",
"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
"doc_to_target": "{{answer if few_shot is undefined else solution}}",
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"samples": "<function list_fewshot_samples at 0x15165204f370>"
},
"num_fewshot": 4,
"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
}
},
"minerva_math_counting_and_prob": {
"task": "minerva_math_counting_and_prob",
"tag": [
"math_word_problems"
],
"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\": normalize_final_answer(\n remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n ),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n return dataset.map(_process_doc)\n",
"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
"doc_to_target": "{{answer if few_shot is undefined else solution}}",
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"samples": "<function list_fewshot_samples at 0x15165204d3f0>"
},
"num_fewshot": 4,
"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
}
},
"minerva_math_geometry": {
"task": "minerva_math_geometry",
"tag": [
"math_word_problems"
],
"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\": normalize_final_answer(\n remove_boxed(last_boxed_only_string(doc[\"solution\"]))\n ),\n }\n if getattr(doc, \"few_shot\", None) is not None:\n out_doc[\"few_shot\"] = True\n return out_doc\n\n return dataset.map(_process_doc)\n",
"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
"doc_to_target": "{{answer if few_shot is undefined else solution}}",
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "first_n",
"samples": "<function list_fewshot_samples at 0x1516516a6170>"
},
"num_fewshot": 4,
"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
}
},
"minerva_math_intermediate_algebra": {
"task": "minerva_math_intermediate_algebra",
"tag": [
"math_word_problems"
],
"group": [
"math_word_problems"
],
"dataset_path": "EleutherAI/hendrycks_math",
"dataset_name": "intermediate_algebra",
"dataset_kwargs": {
"trust_remote_code": true
},
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},
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"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
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},
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"task": "minerva_math_prealgebra",
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],
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},
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"doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n",
"doc_to_target": "{{answer if few_shot is undefined else solution}}",
"process_results": "def process_results(doc: dict, results: List[str]) -> Dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n results = {\n \"exact_match\": retval,\n }\n return results\n",
"description": "",
"target_delimiter": " ",
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}
},
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"tag": [
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"group": [
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},
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"doc_to_target": "{{answer if few_shot is undefined else solution}}",
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}
}
},
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"minerva_math_counting_and_prob": 1.0,
"minerva_math_geometry": 1.0,
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},
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},
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evaluations/en/Qwen2.5-7B-Instruct/mmlu_0_shot.json Normal file
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{
"results": {
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}

112
evaluations/en/Qwen2.5-7B-Instruct/truthfulqa_mc2_0_shot.json Normal file
View File

@@ -0,0 +1,112 @@
{
"results": {
"truthfulqa_mc2": {
"alias": "truthfulqa_mc2",
"acc,none": 0.5893429012938529,
"acc_stderr,none": 0.015852538063666797
}
},
"group_subtasks": {
"truthfulqa_mc2": []
},
"configs": {
"truthfulqa_mc2": {
"task": "truthfulqa_mc2",
"tag": [
"truthfulqa"
],
"dataset_path": "truthful_qa",
"dataset_name": "multiple_choice",
"validation_split": "validation",
"doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question + '\nA:'}}",
"doc_to_target": 0,
"doc_to_choice": "{{mc2_targets.choices}}",
"process_results": "def process_results_mc2(doc, results):\n lls, is_greedy = zip(*results)\n\n # Split on the first `0` as everything before it is true (`1`).\n split_idx = list(doc[\"mc2_targets\"][\"labels\"]).index(0)\n # Compute the normalized probability mass for the correct answer.\n ll_true, ll_false = lls[:split_idx], lls[split_idx:]\n p_true, p_false = np.exp(np.array(ll_true)), np.exp(np.array(ll_false))\n p_true = p_true / (sum(p_true) + sum(p_false))\n\n return {\"acc\": sum(p_true)}\n",
"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": true,
"doc_to_decontamination_query": "question",
"metadata": {
"version": 2.0
}
}
},
"versions": {
"truthfulqa_mc2": 2.0
},
"n-shot": {
"truthfulqa_mc2": 0
},
"higher_is_better": {
"truthfulqa_mc2": {
"acc": true
}
},
"n-samples": {
"truthfulqa_mc2": {
"original": 817,
"effective": 817
}
},
"config": {
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"model_args": "parallelize=True,pretrained=Qwen/Qwen2.5-7B-Instruct,trust_remote_code=True,mm=False,trust_remote_code=True",
"model_num_parameters": 7615616512,
"model_dtype": "torch.bfloat16",
"model_revision": "main",
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"batch_sizes": [],
"device": null,
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"limit": null,
"bootstrap_iters": 100000,
"gen_kwargs": null,
"random_seed": 0,
"numpy_seed": 1234,
"torch_seed": 1234,
"fewshot_seed": 1234
},
"git_hash": "3127d82f",
"date": 1732457573.9086604,
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"tokenizer_pad_token": [
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"model_name_sanitized": "Qwen__Qwen2.5-7B-Instruct",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": null,
"chat_template_sha": null,
"start_time": 939992.680391307,
"end_time": 940617.912944639,
"total_evaluation_time_seconds": "625.2325533319963"
}

112
evaluations/en/Qwen2.5-7B-Instruct/winogrande_0_shot.json Normal file
View File

@@ -0,0 +1,112 @@
{
"results": {
"winogrande": {
"alias": "winogrande",
"acc,none": 0.6937647987371744,
"acc_stderr,none": 0.012954385972802457
}
},
"group_subtasks": {
"winogrande": []
},
"configs": {
"winogrande": {
"task": "winogrande",
"dataset_path": "winogrande",
"dataset_name": "winogrande_xl",
"dataset_kwargs": {
"trust_remote_code": true
},
"training_split": "train",
"validation_split": "validation",
"doc_to_text": "def doc_to_text(doc):\n answer_to_num = {\"1\": 0, \"2\": 1}\n return answer_to_num[doc[\"answer\"]]\n",
"doc_to_target": "def doc_to_target(doc):\n idx = doc[\"sentence\"].index(\"_\") + 1\n return doc[\"sentence\"][idx:].strip()\n",
"doc_to_choice": "def doc_to_choice(doc):\n idx = doc[\"sentence\"].index(\"_\")\n options = [doc[\"option1\"], doc[\"option2\"]]\n return [doc[\"sentence\"][:idx] + opt for opt in options]\n",
"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": true,
"doc_to_decontamination_query": "sentence",
"metadata": {
"version": 1.0
}
}
},
"versions": {
"winogrande": 1.0
},
"n-shot": {
"winogrande": 0
},
"higher_is_better": {
"winogrande": {
"acc": true
}
},
"n-samples": {
"winogrande": {
"original": 1267,
"effective": 1267
}
},
"config": {
"model": "hf",
"model_args": "parallelize=True,pretrained=Qwen/Qwen2.5-7B-Instruct,trust_remote_code=True,mm=False,trust_remote_code=True",
"model_num_parameters": 7615616512,
"model_dtype": "torch.bfloat16",
"model_revision": "main",
"model_sha": "bb46c15ee4bb56c5b63245ef50fd7637234d6f75",
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"device": null,
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"gen_kwargs": null,
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"fewshot_seed": 1234
},
"git_hash": "3127d82f",
"date": 1732457559.179071,
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"151645"
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"None"
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"eot_token_id": 151645,
"max_length": 32768,
"task_hashes": {},
"model_source": "hf",
"model_name": "Qwen/Qwen2.5-7B-Instruct",
"model_name_sanitized": "Qwen__Qwen2.5-7B-Instruct",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": null,
"chat_template_sha": null,
"start_time": 937898.037339817,
"end_time": 938365.062607038,
"total_evaluation_time_seconds": "467.02526722091716"
}