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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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123
evaluations/ar/Mistral-7B-Instruct-v0.3/acva_5_shot.json Normal file
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@@ -0,0 +1,123 @@
{
"results": {
"acva": {
"alias": "acva",
"acc,none": 0.6222732491389208,
"acc_stderr,none": 0.005195116511309794,
"acc_norm,none": 0.6025258323765786,
"acc_norm_stderr,none": 0.005243945200841987
}
},
"group_subtasks": {
"acva": []
},
"configs": {
"acva": {
"task": "acva",
"tag": [
"multiple_choice"
],
"dataset_path": "FreedomIntelligence/ACVA-Arabic-Cultural-Value-Alignment",
"dataset_kwargs": {
"trust_remote_code": true
},
"validation_split": "validation",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _format_subject(subject):\n \n arabic_words = subtasks_ar[subtasks.index(subject)]\n return arabic_words\n \n def _generate_subject(doc):\n subject = _format_subject(doc[\"id\"].split(\"-\")[0])\n\n return subject\n \n def _process_docs(doc):\n keys = [\"\u0635\u062d\",\n \"\u062e\u0637\u0623\"]\n subject = _generate_subject(doc)\n gold = keys.index(doc['answer'])\n out_doc = {\n \"id\": doc[\"id\"],\n \"query\": \"\\n\\n\\n\u0627\u0644\u0633\u0624\u0627\u0644:\" + doc[\"question\"]+\"\\n\u0625\u062c\u0627\u0628\u0629:'\",\n \"choices\": keys,\n \"gold\": gold,\n \"subject\": subject,\n }\n \n return out_doc\n\n return dataset.map(_process_docs)\n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "\u0641\u064a\u0645\u0627 \u064a\u0644\u064a \u0639\u0628\u0627\u0631\u0627\u062a \u0625\u0645\u0627 \u0635\u062d\u064a\u062d\u0629 \u0623\u0648 \u062e\u0627\u0637\u0626\u0629 \u062d\u0648\u0644 {{subject}}\n \u0627\u0644\u0631\u062c\u0627\u0621 \u062a\u0635\u0646\u064a\u0641 \u0627\u0644\u0639\u0628\u0627\u0631\u0629 \u0625\u0644\u0649 '\u0635\u062d' \u0623\u0648 '\u062e\u0637\u0623' \u062f\u0648\u0646 \u0634\u0631\u062d",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 5,
"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": 1.0
}
}
},
"versions": {
"acva": 1.0
},
"n-shot": {
"acva": 5
},
"higher_is_better": {
"acva": {
"acc": true,
"acc_norm": true
}
},
"n-samples": {
"acva": {
"original": 8710,
"effective": 8710
}
},
"config": {
"model": "hf",
"model_args": "pretrained=mistralai/Mistral-7B-Instruct-v0.3,trust_remote_code=True,cache_dir=/tmp,parallelize=False",
"model_num_parameters": 7248023552,
"model_dtype": "torch.bfloat16",
"model_revision": "main",
"model_sha": "e0bc86c23ce5aae1db576c8cca6f06f1f73af2db",
"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": "112b79143",
"date": 1739212726.4606693,
"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 22.04.3 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-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\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): 96\nOn-line CPU(s) list: 0-95\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V12 64-Core Processor\nCPU family: 23\nModel: 49\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 0\nBogoMIPS: 4890.88\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 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 ssbd vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru arat umip rdpid\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 3 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 48 MiB (96 instances)\nL3 cache: 384 MiB (24 instances)\nNUMA node(s): 4\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nNUMA node2 CPU(s): 48-71\nNUMA node3 CPU(s): 72-95\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 disabled\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\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==1.26.4\n[pip3] onnx==1.14.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\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==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect",
"transformers_version": "4.48.3",
"upper_git_hash": null,
"tokenizer_pad_token": [
"<unk>",
"0"
],
"tokenizer_eos_token": [
"</s>",
"2"
],
"tokenizer_bos_token": [
"<s>",
"1"
],
"eot_token_id": 2,
"max_length": 32768,
"task_hashes": {},
"model_source": "hf",
"model_name": "mistralai/Mistral-7B-Instruct-v0.3",
"model_name_sanitized": "mistralai__Mistral-7B-Instruct-v0.3",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": null,
"chat_template_sha": null,
"start_time": 1323160.590628094,
"end_time": 1324067.409366255,
"total_evaluation_time_seconds": "906.8187381608877"
}

142
evaluations/ar/Mistral-7B-Instruct-v0.3/ar_ifeval_0_shot.json Normal file
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@@ -0,0 +1,142 @@
{
"results": {
"ar_ifeval": {
"alias": "ar_ifeval",
"prompt_level_strict_acc,none": 0.3041044776119403,
"prompt_level_strict_acc_stderr,none": 0.019888706432720362,
"inst_level_strict_acc,none": 0.6402730375426621,
"inst_level_strict_acc_stderr,none": "N/A",
"prompt_level_loose_acc,none": 0.3656716417910448,
"prompt_level_loose_acc_stderr,none": 0.020822161638297292,
"inst_level_loose_acc,none": 0.6839590443686007,
"inst_level_loose_acc_stderr,none": "N/A"
}
},
"group_subtasks": {
"ar_ifeval": []
},
"configs": {
"ar_ifeval": {
"task": "ar_ifeval",
"dataset_path": "lm_eval/tasks/ar_ifeval/ar_ifeval.py",
"dataset_name": "ar_ifeval",
"dataset_kwargs": {
"trust_remote_code": true
},
"test_split": "test",
"doc_to_text": "prompt",
"doc_to_target": 0,
"process_results": "def process_results(doc, results):\n\n response = results[0]\n out_strict = process_sample(doc, response, 'strict')\n out_loose = process_sample(doc, response, 'loose')\n\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": {
"ar_ifeval": 4.0
},
"n-shot": {
"ar_ifeval": 0
},
"higher_is_better": {
"ar_ifeval": {
"prompt_level_strict_acc": true,
"inst_level_strict_acc": true,
"prompt_level_loose_acc": true,
"inst_level_loose_acc": true
}
},
"n-samples": {
"ar_ifeval": {
"original": 536,
"effective": 536
}
},
"config": {
"model": "hf",
"model_args": "pretrained=mistralai/Mistral-7B-Instruct-v0.3,trust_remote_code=True,cache_dir=/tmp,parallelize=True",
"model_num_parameters": 7248023552,
"model_dtype": "torch.bfloat16",
"model_revision": "main",
"model_sha": "e0bc86c23ce5aae1db576c8cca6f06f1f73af2db",
"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": "b955b2950",
"date": 1739618660.514274,
"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 22.04.3 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-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\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): 96\nOn-line CPU(s) list: 0-95\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V12 64-Core Processor\nCPU family: 23\nModel: 49\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 0\nBogoMIPS: 4890.88\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 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 ssbd vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru arat umip rdpid\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 3 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 48 MiB (96 instances)\nL3 cache: 384 MiB (24 instances)\nNUMA node(s): 4\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nNUMA node2 CPU(s): 48-71\nNUMA node3 CPU(s): 72-95\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 disabled\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\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==1.26.4\n[pip3] onnx==1.14.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\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==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect",
"transformers_version": "4.48.3",
"upper_git_hash": null,
"tokenizer_pad_token": [
"<unk>",
"0"
],
"tokenizer_eos_token": [
"</s>",
"2"
],
"tokenizer_bos_token": [
"<s>",
"1"
],
"eot_token_id": 2,
"max_length": 32768,
"task_hashes": {
"ar_ifeval": "b8aedf628540509f53512423803c97c0af76f913e1d9c5626e46aceefce168b2"
},
"model_source": "hf",
"model_name": "mistralai/Mistral-7B-Instruct-v0.3",
"model_name_sanitized": "mistralai__Mistral-7B-Instruct-v0.3",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS] [\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST] \" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST] \" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n {{- \"[TOOL_CALLS] [\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + eos_token }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- \" \" + message[\"content\"]|trim + eos_token}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS] {\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n",
"chat_template_sha": "e16746b40344d6c5b5265988e0328a0bf7277be86f1c335156eae07e29c82826",
"start_time": 1583947.032479211,
"end_time": 1595212.6691982,
"total_evaluation_time_seconds": "11265.636718989117"
}

126
evaluations/ar/Mistral-7B-Instruct-v0.3/araMath_v3_5_shot.json Normal file
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@@ -0,0 +1,126 @@
{
"results": {
"araMath_v3": {
"alias": "araMath_v3",
"acc,none": 0.27107438016528923,
"acc_stderr,none": 0.01808703482553977,
"acc_norm,none": 0.27107438016528923,
"acc_norm_stderr,none": 0.01808703482553977
}
},
"group_subtasks": {
"araMath_v3": []
},
"configs": {
"araMath_v3": {
"task": "araMath_v3",
"tag": [
"multiple_choice"
],
"dataset_path": "lm_eval/tasks/araMath_v3/araMath_v3.py",
"dataset_name": "araMath_v3",
"dataset_kwargs": {
"trust_remote_code": true
},
"validation_split": "validation",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n def remove_prefix(choice):\n prefixes = [\"(A)\", \"(B)\", \"(C)\", \"(D)\"]\n for prefix in prefixes:\n if choice.startswith(prefix + \" \"):\n return choice[len(prefix) + 1:] \n return choice \n\n def format_example(doc, keys):\n question = doc[\"question\"].strip()\n choices = \"\".join(\n [f\"{key}. {remove_prefix(choice)}\\n\" for key, choice in zip(keys, doc[\"options\"])]\n )\n\n prompt = f\"\\n\\n\u0627\u0644\u0633\u0624\u0627\u0644: {question}\\n{choices}\\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:\"\n return prompt\n\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_en,\n \"gold\": keys_en.index(doc[\"label\"]),\n }\n return out_doc\n \n return dataset.map(_process_docs)\n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "{{choices}}",
"description": "\u0645\u0646 \u0641\u0636\u0644\u0643 \u0627\u062e\u062a\u0631 \u0625\u062c\u0627\u0628\u0629 \u0648\u0627\u062d\u062f\u0629 \u0645\u0646 \u0628\u064a\u0646 'A\u060c B\u060c C\u060c D' \u062f\u0648\u0646 \u0634\u0631\u062d",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 5,
"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": "query",
"metadata": {
"version": 0.0
}
}
},
"versions": {
"araMath_v3": 0.0
},
"n-shot": {
"araMath_v3": 5
},
"higher_is_better": {
"araMath_v3": {
"acc": true,
"acc_norm": true
}
},
"n-samples": {
"araMath_v3": {
"original": 605,
"effective": 605
}
},
"config": {
"model": "hf",
"model_args": "pretrained=mistralai/Mistral-7B-Instruct-v0.3,trust_remote_code=True,cache_dir=/tmp,parallelize=True",
"model_num_parameters": 7248023552,
"model_dtype": "torch.bfloat16",
"model_revision": "main",
"model_sha": "e0bc86c23ce5aae1db576c8cca6f06f1f73af2db",
"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": "b955b2950",
"date": 1739618557.9082067,
"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 22.04.3 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-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\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): 96\nOn-line CPU(s) list: 0-95\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V12 64-Core Processor\nCPU family: 23\nModel: 49\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 0\nBogoMIPS: 4890.88\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 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 ssbd vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru arat umip rdpid\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 3 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 48 MiB (96 instances)\nL3 cache: 384 MiB (24 instances)\nNUMA node(s): 4\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nNUMA node2 CPU(s): 48-71\nNUMA node3 CPU(s): 72-95\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 disabled\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\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==1.26.4\n[pip3] onnx==1.14.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\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==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect",
"transformers_version": "4.48.3",
"upper_git_hash": null,
"tokenizer_pad_token": [
"<unk>",
"0"
],
"tokenizer_eos_token": [
"</s>",
"2"
],
"tokenizer_bos_token": [
"<s>",
"1"
],
"eot_token_id": 2,
"max_length": 32768,
"task_hashes": {
"araMath_v3": "fc6325d1e91d814a9212e7cd3d01a2ea0128526a5ff5a12b13029293c7b85a14"
},
"model_source": "hf",
"model_name": "mistralai/Mistral-7B-Instruct-v0.3",
"model_name_sanitized": "mistralai__Mistral-7B-Instruct-v0.3",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS] [\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST] \" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST] \" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n {{- \"[TOOL_CALLS] [\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + eos_token }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- \" \" + message[\"content\"]|trim + eos_token}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS] {\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n",
"chat_template_sha": "e16746b40344d6c5b5265988e0328a0bf7277be86f1c335156eae07e29c82826",
"start_time": 1583844.288660905,
"end_time": 1583895.209942275,
"total_evaluation_time_seconds": "50.921281369868666"
}

130
evaluations/ar/Mistral-7B-Instruct-v0.3/araPro_0_shot.json Normal file
View File

@@ -0,0 +1,130 @@
{
"results": {
"araPro": {
"alias": "araPro",
"acc,none": 0.4385122975404919,
"acc_stderr,none": 0.007017396418135006,
"acc_norm,none": 0.4385122975404919,
"acc_norm_stderr,none": 0.007017396418135006
}
},
"group_subtasks": {
"araPro": []
},
"configs": {
"araPro": {
"task": "araPro",
"tag": [
"multiple_choice"
],
"dataset_path": "lm_eval/tasks/araPro/araPro.py",
"dataset_name": "araPro",
"dataset_kwargs": {
"trust_remote_code": true
},
"validation_split": "validation",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc): \n def remove_prefix(choice):\n return choice.replace('.', '') if '.' in choice[:2] else choice\n \n def format_example(doc, keys):\n question = doc[\"question\"].strip()\n \n choice_num = ['choice1', 'choice2', 'choice3', 'choice4']\n choices = \"\".join(\n [f\"{key}. {remove_prefix(doc[choice_num[index]])}\\n\" for index, key in enumerate(keys)]\n )\n\n prompt = f\"\\n\\n\\n\u0627\u0644\u0633\u0624\u0627\u0644: {question}\\n{choices} \\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:\"\n return prompt\n\n #keys = [\"1\", \"2\", \"3\", \"4\"]\n keys = [\"A\", \"B\", \"C\", \"D\"]\n out_doc = {\n \"query\": format_example(doc, keys), \n \"choices\": keys,\n \"gold\": doc[\"answer\"]-1,\n } \n\n return out_doc\n \n return dataset.map(_process_docs)\n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "{{choices}}",
"description": "\u0641\u064a\u0645\u0627 \u064a\u0644\u064a \u0623\u0633\u0626\u0644\u0629 \u0627\u0644\u0627\u062e\u062a\u064a\u0627\u0631 \u0645\u0646 \u0645\u062a\u0639\u062f\u062f (\u0645\u0639 \u0627\u0644\u0625\u062c\u0627\u0628\u0627\u062a) \u0645\u0646 \u0641\u0636\u0644\u0643 \u0627\u062e\u062a\u0631 \u0625\u062c\u0627\u0628\u0629 \u0648\u0627\u062d\u062f\u0629 \u062f\u0648\u0646 \u0634\u0631\u062d",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "balanced_cat"
},
"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",
"metadata": {
"version": 2.0
}
}
},
"versions": {
"araPro": 2.0
},
"n-shot": {
"araPro": 0
},
"higher_is_better": {
"araPro": {
"acc": true,
"acc_norm": true
}
},
"n-samples": {
"araPro": {
"original": 5001,
"effective": 5001
}
},
"config": {
"model": "hf",
"model_args": "pretrained=mistralai/Mistral-7B-Instruct-v0.3,trust_remote_code=True,cache_dir=/tmp,parallelize=True",
"model_num_parameters": 7248023552,
"model_dtype": "torch.bfloat16",
"model_revision": "main",
"model_sha": "e0bc86c23ce5aae1db576c8cca6f06f1f73af2db",
"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": "b955b2950",
"date": 1739617070.2494006,
"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 22.04.3 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-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\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): 96\nOn-line CPU(s) list: 0-95\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V12 64-Core Processor\nCPU family: 23\nModel: 49\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 0\nBogoMIPS: 4890.88\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 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 ssbd vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru arat umip rdpid\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 3 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 48 MiB (96 instances)\nL3 cache: 384 MiB (24 instances)\nNUMA node(s): 4\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nNUMA node2 CPU(s): 48-71\nNUMA node3 CPU(s): 72-95\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 disabled\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\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==1.26.4\n[pip3] onnx==1.14.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\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==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect",
"transformers_version": "4.48.3",
"upper_git_hash": null,
"tokenizer_pad_token": [
"<unk>",
"0"
],
"tokenizer_eos_token": [
"</s>",
"2"
],
"tokenizer_bos_token": [
"<s>",
"1"
],
"eot_token_id": 2,
"max_length": 32768,
"task_hashes": {
"araPro": "199097343993a3034793f07adc5e21cca4b5d4e6175f4b73353037c1f92be7cc"
},
"model_source": "hf",
"model_name": "mistralai/Mistral-7B-Instruct-v0.3",
"model_name_sanitized": "mistralai__Mistral-7B-Instruct-v0.3",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS] [\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST] \" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST] \" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n {{- \"[TOOL_CALLS] [\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + eos_token }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- \" \" + message[\"content\"]|trim + eos_token}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS] {\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n",
"chat_template_sha": "e16746b40344d6c5b5265988e0328a0bf7277be86f1c335156eae07e29c82826",
"start_time": 1582356.635046546,
"end_time": 1582870.225454165,
"total_evaluation_time_seconds": "513.5904076187871"
}

2045
evaluations/ar/Mistral-7B-Instruct-v0.3/arabicmmlu_0_shot.json Normal file
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evaluations/ar/Mistral-7B-Instruct-v0.3/etec_v2_0_shot.json Normal file
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{
"results": {
"etec_v2": {
"alias": "etec_v2",
"acc,none": 0.3566507684154743,
"acc_stderr,none": 0.01102996491785656,
"acc_norm,none": 0.3566507684154743,
"acc_norm_stderr,none": 0.01102996491785656
}
},
"group_subtasks": {
"etec_v2": []
},
"configs": {
"etec_v2": {
"task": "etec_v2",
"tag": [
"multiple_choice"
],
"dataset_path": "lm_eval/tasks/etec_v2/etec.py",
"dataset_name": "etec_v2",
"dataset_kwargs": {
"trust_remote_code": true
},
"validation_split": "validation",
"test_split": "test",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n def format_example(doc, keys):\n question = doc[\"question\"].strip()\n \n choices = \"\".join(\n [f\"{key}. {choice}\\n\" for key, choice in zip(keys, doc[\"choices\"])]\n )\n prompt = f\"\u0627\u0644\u0633\u0624\u0627\u0644: {question}\\n{choices}\\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:\"\n return prompt\n print(doc[\"label\"])\n keys_ar = [\"\u0623\", \"\u0628\", \"\u062c\", \"\u062f\"]\n keys_en = [\"A\", \"B\", \"C\", \"D\"]\n out_doc = {\n \"query\": format_example(doc, keys_en),\n \"choices\": keys_en,\n \"gold\": int(doc[\"label\"])-1,\n }\n return out_doc\n \n return dataset.map(_process_docs)\n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "\u0641\u064a\u0645\u0627 \u064a\u0644\u064a \u0623\u0633\u0626\u0644\u0629 \u0627\u0644\u0627\u062e\u062a\u064a\u0627\u0631 \u0645\u0646 \u0645\u062a\u0639\u062f\u062f (\u0645\u0639 \u0627\u0644\u0625\u062c\u0627\u0628\u0627\u062a) \u0645\u0646 \u0641\u0636\u0644\u0643 \u0627\u062e\u062a\u0631 \u0625\u062c\u0627\u0628\u0629 \u0648\u0627\u062d\u062f\u0629 \u062f\u0648\u0646 \u0634\u0631\u062d\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": true,
"doc_to_decontamination_query": "query",
"metadata": {
"version": 0.0
}
}
},
"versions": {
"etec_v2": 0.0
},
"n-shot": {
"etec_v2": 0
},
"higher_is_better": {
"etec_v2": {
"acc": true,
"acc_norm": true
}
},
"n-samples": {
"etec_v2": {
"original": 1887,
"effective": 1887
}
},
"config": {
"model": "hf",
"model_args": "pretrained=mistralai/Mistral-7B-Instruct-v0.3,trust_remote_code=True,cache_dir=/tmp,parallelize=True",
"model_num_parameters": 7248023552,
"model_dtype": "torch.bfloat16",
"model_revision": "main",
"model_sha": "e0bc86c23ce5aae1db576c8cca6f06f1f73af2db",
"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": "b955b2950",
"date": 1739617648.4240222,
"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 22.04.3 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-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\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): 96\nOn-line CPU(s) list: 0-95\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V12 64-Core Processor\nCPU family: 23\nModel: 49\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 0\nBogoMIPS: 4890.88\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 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 ssbd vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru arat umip rdpid\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 3 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 48 MiB (96 instances)\nL3 cache: 384 MiB (24 instances)\nNUMA node(s): 4\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nNUMA node2 CPU(s): 48-71\nNUMA node3 CPU(s): 72-95\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 disabled\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\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==1.26.4\n[pip3] onnx==1.14.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\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==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect",
"transformers_version": "4.48.3",
"upper_git_hash": null,
"tokenizer_pad_token": [
"<unk>",
"0"
],
"tokenizer_eos_token": [
"</s>",
"2"
],
"tokenizer_bos_token": [
"<s>",
"1"
],
"eot_token_id": 2,
"max_length": 32768,
"task_hashes": {
"etec_v2": "8dd4f73e94b492d082eebafc44fe527d605540255eaf869f23c7d51e4ffb37c4"
},
"model_source": "hf",
"model_name": "mistralai/Mistral-7B-Instruct-v0.3",
"model_name_sanitized": "mistralai__Mistral-7B-Instruct-v0.3",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS] [\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST] \" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST] \" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n {{- \"[TOOL_CALLS] [\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + eos_token }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- \" \" + message[\"content\"]|trim + eos_token}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS] {\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n",
"chat_template_sha": "e16746b40344d6c5b5265988e0328a0bf7277be86f1c335156eae07e29c82826",
"start_time": 1582935.013239375,
"end_time": 1583016.72552446,
"total_evaluation_time_seconds": "81.71228508488275"
}

125
evaluations/ar/Mistral-7B-Instruct-v0.3/exams_ar_5_shot.json Normal file
View File

@@ -0,0 +1,125 @@
{
"results": {
"exams_ar": {
"alias": "exams_ar",
"acc,none": 0.3407821229050279,
"acc_stderr,none": 0.02047248187699896,
"acc_norm,none": 0.3407821229050279,
"acc_norm_stderr,none": 0.02047248187699896
}
},
"group_subtasks": {
"exams_ar": []
},
"configs": {
"exams_ar": {
"task": "exams_ar",
"tag": [
"multiple_choice"
],
"dataset_path": "lm_eval/tasks/exams_ar",
"dataset_name": "exams_ar",
"dataset_kwargs": {
"trust_remote_code": true
},
"validation_split": "validation",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n\n def _process_docs(doc):\n def format_example(doc, keys):\n \"\"\"\n <prompt>\n \u0633\u0624\u0627\u0644:\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n \u0627\u062c\u0627\u0628\u0629:\n \"\"\"\n \n question = doc[\"question\"].strip()\n \n choices = \"\".join(\n [f\"{key}. {choice}\\n\" for key, choice in zip(keys, doc[\"choices\"])]\n )\n prompt = f\"\u0627\u0644\u0633\u0624\u0627\u0644: {question}\\n{choices} \\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:\"\n return prompt\n\n def _format_subject(subject):\n arabic_words = subtasks_ar[subtasks.index(subject)]\n return arabic_words\n\n keys = [\"A\", \"B\", \"C\", \"D\"]\n \n subject = doc['id'].split(\"-\")[0]\n description = f\"\ufed2\ufef4\ufee3\ufe8d \ufef2\ufee0\ufef3 \ufe84\ufeb4\ufe8c\ufedf\ufe93 \ufe8d\ufefc\ufea8\ufe98\ufef3\ufe8d\ufead \ufee2\ufee7 \ufee2\ufe98\ufecb\ufea9\ufea9 (\ufee2\ufecb \ufe8d\ufefa\ufe9f\ufe8e\ufe91\ufe8e\ufe97) \ufea1\ufeee\ufedf {_format_subject(subject)} \\n\" #\ufee2\ufee7 \ufed2\ufec0\ufee0\ufedb \ufe8e\ufea8\ufe97\ufead \ufe88\ufe9f\ufe8e\ufe91\ufe93 \ufeed\ufe8e\ufea3\ufea9\ufe93 \ufee2\ufee7 \ufe90\ufef4\ufee7 'A\u060c B\u060c C\u060c D' \ufea9\ufeee\ufee7 \ufeb5\ufeae\ufea3\\n\"\n\n out_doc = {\n \"idx\": doc[\"idx\"],\n \"id\": doc[\"id\"],\n 'dsecription': description,\n \"query\": format_example(doc, keys), # \"Question: \" + doc[\"question\"]['stem'] + \"\\nAnswer:\",\n \"choices\": keys,\n \"gold\": [\"A\", \"B\", \"C\", \"D\"].index(doc[\"label\"]),\n }\n return out_doc\n\n return dataset.map(_process_docs)\n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "description",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 5,
"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": "query",
"metadata": {
"version": 1.0
}
}
},
"versions": {
"exams_ar": 1.0
},
"n-shot": {
"exams_ar": 5
},
"higher_is_better": {
"exams_ar": {
"acc": true,
"acc_norm": true
}
},
"n-samples": {
"exams_ar": {
"original": 537,
"effective": 537
}
},
"config": {
"model": "hf",
"model_args": "pretrained=mistralai/Mistral-7B-Instruct-v0.3,trust_remote_code=True,cache_dir=/tmp,parallelize=False",
"model_num_parameters": 7248023552,
"model_dtype": "torch.bfloat16",
"model_revision": "main",
"model_sha": "e0bc86c23ce5aae1db576c8cca6f06f1f73af2db",
"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": "112b79143",
"date": 1739211970.5611851,
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543
evaluations/ar/Mistral-7B-Instruct-v0.3/gat_0_shot.json Normal file
View File

@@ -0,0 +1,543 @@
{
"results": {
"gat": {
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"acc_stderr,none": 0.003495353970358859,
"alias": "gat"
},
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},
"gat_analogy": {
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"acc_stderr,none": 0.00873445255221157
},
"gat_arithmetic": {
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},
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},
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"acc,none": 0.30573770491803276,
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},
"gat_completion": {
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},
"gat_contextual": {
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"acc_stderr,none": 0.012092310807729188
},
"gat_geometry": {
"alias": " - gat_geometry",
"acc,none": 0.25205479452054796,
"acc_stderr,none": 0.022757873597035808
},
"gat_reading": {
"alias": " - gat_reading",
"acc,none": 0.2729678638941399,
"acc_stderr,none": 0.008663668753419975
}
},
"groups": {
"gat": {
"acc,none": 0.2664618086040386,
"acc_stderr,none": 0.003495353970358859,
"alias": "gat"
}
},
"group_subtasks": {
"gat": [
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"gat_reading",
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"gat_arithmetic",
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"gat_contextual",
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},
"configs": {
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"dataset_path": "lm_eval/tasks/gat/gat_data/gat.py",
"dataset_name": "algebra",
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},
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n # def _process_doc(doc):\n \n # subject = doc['id'].split(\"-\")[0]\n # subject_ar = subtasks_ar[subtasks.index(subject)]\n # out_doc = {**doc, 'subject_ar': subject_ar}\n # print(subject_ar)\n # print(out_doc)\n # return out_doc\n\n return dataset\n",
"doc_to_text": "{{question}}\n\u0623. {{choices[0]}}\n\u0628. {{choices[1]}}\n\u062c. {{choices[2]}}\n\u062f. {{choices[3]}}\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:",
"doc_to_target": "{{label}}",
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"description": "",
"target_delimiter": " ",
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],
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"should_decontaminate": false,
"metadata": {
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}
},
"gat_analogy": {
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"dataset_name": "analogy",
"dataset_kwargs": {
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},
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n # def _process_doc(doc):\n \n # subject = doc['id'].split(\"-\")[0]\n # subject_ar = subtasks_ar[subtasks.index(subject)]\n # out_doc = {**doc, 'subject_ar': subject_ar}\n # print(subject_ar)\n # print(out_doc)\n # return out_doc\n\n return dataset\n",
"doc_to_text": "{{question}}\n\u0623. {{choices[0]}}\n\u0628. {{choices[1]}}\n\u062c. {{choices[2]}}\n\u062f. {{choices[3]}}\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:",
"doc_to_target": "{{label}}",
"doc_to_choice": [
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"\u062f"
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"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
"metric_list": [
{
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"aggregation": "mean",
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}
],
"output_type": "multiple_choice",
"repeats": 1,
"should_decontaminate": false,
"metadata": {
"version": 0.0
}
},
"gat_arithmetic": {
"task": "gat_arithmetic",
"dataset_path": "lm_eval/tasks/gat/gat_data/gat.py",
"dataset_name": "arithmetic",
"dataset_kwargs": {
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},
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n # def _process_doc(doc):\n \n # subject = doc['id'].split(\"-\")[0]\n # subject_ar = subtasks_ar[subtasks.index(subject)]\n # out_doc = {**doc, 'subject_ar': subject_ar}\n # print(subject_ar)\n # print(out_doc)\n # return out_doc\n\n return dataset\n",
"doc_to_text": "{{question}}\n\u0623. {{choices[0]}}\n\u0628. {{choices[1]}}\n\u062c. {{choices[2]}}\n\u062f. {{choices[3]}}\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:",
"doc_to_target": "{{label}}",
"doc_to_choice": [
"\u0623",
"\u0628",
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"\u062f"
],
"description": "",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"num_fewshot": 0,
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{
"metric": "acc",
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"higher_is_better": true
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],
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"should_decontaminate": false,
"metadata": {
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}
},
"gat_association": {
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"dataset_name": "association",
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},
"test_split": "test",
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n # def _process_doc(doc):\n \n # subject = doc['id'].split(\"-\")[0]\n # subject_ar = subtasks_ar[subtasks.index(subject)]\n # out_doc = {**doc, 'subject_ar': subject_ar}\n # print(subject_ar)\n # print(out_doc)\n # return out_doc\n\n return dataset\n",
"doc_to_text": "{{question}}\n\u0623. {{choices[0]}}\n\u0628. {{choices[1]}}\n\u062c. {{choices[2]}}\n\u062f. {{choices[3]}}\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:",
"doc_to_target": "{{label}}",
"doc_to_choice": [
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"description": "",
"target_delimiter": " ",
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"metric_list": [
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],
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"metadata": {
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}
},
"gat_comparisons": {
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"dataset_name": "comparisons",
"dataset_kwargs": {
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},
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n # def _process_doc(doc):\n \n # subject = doc['id'].split(\"-\")[0]\n # subject_ar = subtasks_ar[subtasks.index(subject)]\n # out_doc = {**doc, 'subject_ar': subject_ar}\n # print(subject_ar)\n # print(out_doc)\n # return out_doc\n\n return dataset\n",
"doc_to_text": "{{question}}\n\u0623. {{choices[0]}}\n\u0628. {{choices[1]}}\n\u062c. {{choices[2]}}\n\u062f. {{choices[3]}}\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:",
"doc_to_target": "{{label}}",
"doc_to_choice": [
"\u0623",
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"\u062c",
"\u062f"
],
"description": "",
"target_delimiter": " ",
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{
"metric": "acc",
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}
],
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"metadata": {
"version": 0.0
}
},
"gat_completion": {
"task": "gat_completion",
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"dataset_name": "completion",
"dataset_kwargs": {
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},
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"fewshot_split": "validation",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n # def _process_doc(doc):\n \n # subject = doc['id'].split(\"-\")[0]\n # subject_ar = subtasks_ar[subtasks.index(subject)]\n # out_doc = {**doc, 'subject_ar': subject_ar}\n # print(subject_ar)\n # print(out_doc)\n # return out_doc\n\n return dataset\n",
"doc_to_text": "{{question}}\n\u0623. {{choices[0]}}\n\u0628. {{choices[1]}}\n\u062c. {{choices[2]}}\n\u062f. {{choices[3]}}\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:",
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],
"description": "",
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}
],
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"metadata": {
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},
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"dataset_name": "contextual",
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},
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n # def _process_doc(doc):\n \n # subject = doc['id'].split(\"-\")[0]\n # subject_ar = subtasks_ar[subtasks.index(subject)]\n # out_doc = {**doc, 'subject_ar': subject_ar}\n # print(subject_ar)\n # print(out_doc)\n # return out_doc\n\n return dataset\n",
"doc_to_text": "{{question}}\n\u0623. {{choices[0]}}\n\u0628. {{choices[1]}}\n\u062c. {{choices[2]}}\n\u062f. {{choices[3]}}\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:",
"doc_to_target": "{{label}}",
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"\u0628",
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"\u062f"
],
"description": "\u0627\u0648\u062c\u062f \u0627\u0644\u062e\u0637\u0623 \u0627\u0644\u0633\u064a\u0627\u0642\u064a \u0641\u064a \u0627\u0644\u0639\u0628\u0627\u0631\u0629 \u0627\u0644\u062a\u0627\u0644\u064a\u0629 \u0645\u0646 \u0628\u064a\u0646 \u0627\u0644\u062e\u064a\u0627\u0631\u0627\u062a:",
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"metric_list": [
{
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}
],
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"metadata": {
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}
},
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},
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],
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}
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},
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},
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"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n # def _process_doc(doc):\n \n # subject = doc['id'].split(\"-\")[0]\n # subject_ar = subtasks_ar[subtasks.index(subject)]\n # out_doc = {**doc, 'subject_ar': subject_ar}\n # print(subject_ar)\n # print(out_doc)\n # return out_doc\n\n return dataset\n",
"doc_to_text": "{{question}}\n\u0623. {{choices[0]}}\n\u0628. {{choices[1]}}\n\u062c. {{choices[2]}}\n\u062f. {{choices[3]}}\n\u0627\u0644\u0627\u062c\u0627\u0628\u0629:",
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"metadata": {
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}
},
"versions": {
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},
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},
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},
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"effective": 1220
},
"gat_contextual": {
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"effective": 1304
},
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"effective": 365
}
},
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"model_num_parameters": 7248023552,
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"numpy_seed": 1234,
"torch_seed": 1234,
"fewshot_seed": 1234
},
"git_hash": "112b79143",
"date": 1739240499.1300695,
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],
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"max_length": 32768,
"task_hashes": {},
"model_source": "hf",
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"model_name_sanitized": "mistralai__Mistral-7B-Instruct-v0.3",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": null,
"chat_template_sha": null,
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}

127
evaluations/ar/Mistral-7B-Instruct-v0.3/moe_ien_mcq_0_shot.json Normal file
View File

@@ -0,0 +1,127 @@
{
"results": {
"moe_ien_mcq": {
"alias": "moe_ien_mcq",
"acc,none": 0.5359359359359359,
"acc_stderr,none": 0.004989814518061573,
"acc_norm,none": 0.5359359359359359,
"acc_norm_stderr,none": 0.004989814518061573
}
},
"group_subtasks": {
"moe_ien_mcq": []
},
"configs": {
"moe_ien_mcq": {
"task": "moe_ien_mcq",
"dataset_path": "lm_eval/tasks/moe_ien_mcq/ien_moe_mcq.py",
"dataset_name": "moe_ien_mcq",
"dataset_kwargs": {
"trust_remote_code": true
},
"validation_split": "validation",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc): \n def remove_prefix(choice):\n return choice.split(\". \", 1)[1] if \". \" in choice else choice\n\n def format_example(doc, keys):\n question = doc[\"Question\"].strip()\n \n choices = \"\".join(\n [f\"{key}. {remove_prefix(choice)}\\n\" for key, choice in zip(keys, doc[\"Choices\"])]\n \n )\n prompt = f\"\\n\\n\u0633\u0624\u0627\u0644: {question}\\n{choices} \\n\u0627\u062c\u0627\u0628\u0629:\"\n return prompt\n\n keys = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\"][0:len(doc[\"Choices\"])]\n out_doc = {\n \"Query\": format_example(doc, keys), \n \"Choices\": keys,\n \"gold\": int(doc[\"Answer\"])-1, ## \n } \n return out_doc\n \n return dataset.map(_process_docs)\n",
"doc_to_text": "Query",
"doc_to_target": "gold",
"doc_to_choice": "{{Choices}}",
"description": "\u0641\u064a\u0645\u0627\u202f\u064a\u0644\u064a\u202f\u0623\u0633\u0626\u0644\u0629\u202f\u0627\u0644\u0627\u062e\u062a\u064a\u0627\u0631\u202f\u0645\u0646\u202f\u0645\u062a\u0639\u062f\u062f\u202f(\u0645\u0639\u202f\u0627\u0644\u0625\u062c\u0627\u0628\u0627\u062a)\u202f\u0641\u064a\u202f{{Subject}}",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "balanced_cat"
},
"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": "Query",
"metadata": {
"version": 0.0
}
}
},
"versions": {
"moe_ien_mcq": 0.0
},
"n-shot": {
"moe_ien_mcq": 0
},
"higher_is_better": {
"moe_ien_mcq": {
"acc": true,
"acc_norm": true
}
},
"n-samples": {
"moe_ien_mcq": {
"original": 9990,
"effective": 9990
}
},
"config": {
"model": "hf",
"model_args": "pretrained=mistralai/Mistral-7B-Instruct-v0.3,trust_remote_code=True,cache_dir=/tmp,parallelize=True",
"model_num_parameters": 7248023552,
"model_dtype": "torch.bfloat16",
"model_revision": "main",
"model_sha": "e0bc86c23ce5aae1db576c8cca6f06f1f73af2db",
"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": "b955b2950",
"date": 1739617801.8553765,
"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 22.04.3 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-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\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): 96\nOn-line CPU(s) list: 0-95\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V12 64-Core Processor\nCPU family: 23\nModel: 49\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 0\nBogoMIPS: 4890.88\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 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 ssbd vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru arat umip rdpid\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 3 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 48 MiB (96 instances)\nL3 cache: 384 MiB (24 instances)\nNUMA node(s): 4\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nNUMA node2 CPU(s): 48-71\nNUMA node3 CPU(s): 72-95\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 disabled\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\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==1.26.4\n[pip3] onnx==1.14.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\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==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect",
"transformers_version": "4.48.3",
"upper_git_hash": null,
"tokenizer_pad_token": [
"<unk>",
"0"
],
"tokenizer_eos_token": [
"</s>",
"2"
],
"tokenizer_bos_token": [
"<s>",
"1"
],
"eot_token_id": 2,
"max_length": 32768,
"task_hashes": {
"moe_ien_mcq": "7b5b044e4260d8f2ccd928941529cc6f13c02303af5ed0b926cb22069d0a3368"
},
"model_source": "hf",
"model_name": "mistralai/Mistral-7B-Instruct-v0.3",
"model_name_sanitized": "mistralai__Mistral-7B-Instruct-v0.3",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS] [\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST] \" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST] \" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n {{- \"[TOOL_CALLS] [\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + eos_token }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- \" \" + message[\"content\"]|trim + eos_token}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS] {\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n",
"chat_template_sha": "e16746b40344d6c5b5265988e0328a0bf7277be86f1c335156eae07e29c82826",
"start_time": 1583088.400197009,
"end_time": 1583390.481922052,
"total_evaluation_time_seconds": "302.08172504301183"
}

129
evaluations/ar/Mistral-7B-Instruct-v0.3/moe_ien_tf_0_shot.json Normal file
View File

@@ -0,0 +1,129 @@
{
"results": {
"moe_ien_tf": {
"alias": "moe_ien_tf",
"acc,none": 0.6340374377468658,
"acc_stderr,none": 0.0063130565613714554,
"acc_norm,none": 0.6340374377468658,
"acc_norm_stderr,none": 0.0063130565613714554
}
},
"group_subtasks": {
"moe_ien_tf": []
},
"configs": {
"moe_ien_tf": {
"task": "moe_ien_tf",
"tag": [
"multiple_choice"
],
"dataset_path": "lm_eval/tasks/moe_ien_tf/moe_ien_tf.py",
"dataset_name": "moe_ien_tf",
"dataset_kwargs": {
"trust_remote_code": true
},
"validation_split": "validation",
"test_split": "test",
"fewshot_split": "validation",
"process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_docs(doc):\n keys=[\"\u0635\u062d\u064a\u062d\u0629\",\n \"\u062e\u0627\u0637\u0626\u0629\"\n ]\n #keys =[\"\u0635\u0648\u0627\u0628\",\n # \"\u062e\u0637\u0623\"]\n target_key = int(doc[\"Answer\"])-1\n\n out_doc = {\n \"query\": \"\\n\\n\u0627\u0644\u0633\u0624\u0627\u0644:\" +doc[\"Question\"]+\"\\n\u0625\u062c\u0627\u0628\u0629:'\", \n \"choices\": keys,\n \"gold\": target_key,\n }\n return out_doc\n return dataset.map(_process_docs)\n",
"doc_to_text": "query",
"doc_to_target": "gold",
"doc_to_choice": "choices",
"description": "\u0641\u064a\u0645\u0627 \u064a\u0644\u064a \u0639\u0628\u0627\u0631\u0627\u062a \u0625\u0645\u0627 \u0635\u062d\u064a\u062d\u0629 \u0623\u0648 \u062e\u0627\u0637\u0626\u0629 \u062d\u0648\u0644 {{Subject}}\n \u0627\u0644\u0631\u062c\u0627\u0621 \u062a\u0635\u0646\u064a\u0641 \u0627\u0644\u0639\u0628\u0627\u0631\u0629 \u0625\u0644\u0649 '\u0635\u062d\u064a\u062d\u0629' \u0623\u0648 '\u062e\u0627\u0637\u0626\u0629' \u062f\u0648\u0646 \u0634\u0631\u062d ",
"target_delimiter": " ",
"fewshot_delimiter": "\n\n",
"fewshot_config": {
"sampler": "balanced_cat"
},
"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": {
"moe_ien_tf": 2.0
},
"n-shot": {
"moe_ien_tf": 0
},
"higher_is_better": {
"moe_ien_tf": {
"acc": true,
"acc_norm": true
}
},
"n-samples": {
"moe_ien_tf": {
"original": 5823,
"effective": 5823
}
},
"config": {
"model": "hf",
"model_args": "pretrained=mistralai/Mistral-7B-Instruct-v0.3,trust_remote_code=True,cache_dir=/tmp,parallelize=True",
"model_num_parameters": 7248023552,
"model_dtype": "torch.bfloat16",
"model_revision": "main",
"model_sha": "e0bc86c23ce5aae1db576c8cca6f06f1f73af2db",
"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": "b955b2950",
"date": 1739618159.7425826,
"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 22.04.3 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-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\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): 96\nOn-line CPU(s) list: 0-95\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V12 64-Core Processor\nCPU family: 23\nModel: 49\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 0\nBogoMIPS: 4890.88\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 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 ssbd vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru arat umip rdpid\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 3 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 48 MiB (96 instances)\nL3 cache: 384 MiB (24 instances)\nNUMA node(s): 4\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nNUMA node2 CPU(s): 48-71\nNUMA node3 CPU(s): 72-95\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 disabled\nVulnerability Spec rstack overflow: Mitigation; safe RET, no microcode\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\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==1.26.4\n[pip3] onnx==1.14.0\n[pip3] pytorch-lightning==2.0.7\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.4.0\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==0.19.0\n[pip3] triton==3.0.0\n[conda] Could not collect",
"transformers_version": "4.48.3",
"upper_git_hash": null,
"tokenizer_pad_token": [
"<unk>",
"0"
],
"tokenizer_eos_token": [
"</s>",
"2"
],
"tokenizer_bos_token": [
"<s>",
"1"
],
"eot_token_id": 2,
"max_length": 32768,
"task_hashes": {
"moe_ien_tf": "87c1341e70cacc508279240f78ecd4d5d873569e238982ef3f15031c20f834da"
},
"model_source": "hf",
"model_name": "mistralai/Mistral-7B-Instruct-v0.3",
"model_name_sanitized": "mistralai__Mistral-7B-Instruct-v0.3",
"system_instruction": null,
"system_instruction_sha": null,
"fewshot_as_multiturn": false,
"chat_template": "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS] [\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST] \" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST] \" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n {{- \"[TOOL_CALLS] [\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + eos_token }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- \" \" + message[\"content\"]|trim + eos_token}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS] {\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n",
"chat_template_sha": "e16746b40344d6c5b5265988e0328a0bf7277be86f1c335156eae07e29c82826",
"start_time": 1583446.306152082,
"end_time": 1583776.933878196,
"total_evaluation_time_seconds": "330.62772611388937"
}

2660
evaluations/ar/Mistral-7B-Instruct-v0.3/openaimmlu_0_shot.json Normal file
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