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{ "gsm8k": { "alias": "gsm8k", "exact_match,strict-match": 0, "exact_match_stderr,strict-match": 0, "exact_match,flexible-extract": 0.12351130737321023, "exact_match_stderr,flexible-extract": 0.003806344450172962 } }
{ "gsm8k": [] }
{ "gsm8k": { "task": "gsm8k", "tag": [ "math_word_problems" ], "dataset_path": "gsm8k", "dataset_name": "main", "training_split": "train", "test_split": "train", "fewshot_split": "train", "doc_to_text": "Question: {{question}}\nAnswer:", "doc_to_target": "{{answer}}", "description": "", "target_delimiter": " ", "fewshot_delimiter": "\n\n", "num_fewshot": 0, "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, "top_p": 1 }, "repeats": 1, "filter_list": [ { "name": "strict-match", "filter": [ { "function": "regex", "regex_pattern": "#### (\\-?[0-9\\.\\,]+)", "group_select": null }, { "function": "take_first", "regex_pattern": null, "group_select": null } ] }, { "name": "flexible-extract", "filter": [ { "function": "regex", "regex_pattern": "(-?[$0-9.,]{2,})|(-?[0-9]+)", "group_select": -1 }, { "function": "take_first", "regex_pattern": null, "group_select": null } ] } ], "should_decontaminate": false, "metadata": { "version": 3 } } }
{ "gsm8k": 3 }
{ "gsm8k": 0 }
{ "gsm8k": { "exact_match": true } }
{ "gsm8k": { "original": 7473, "effective": 7473 } }
{ "model": "hf", "model_args": "pretrained=meta-llama/Llama-3.2-3B,dtype=float16", "model_num_parameters": 3212749824, "model_dtype": "torch.float16", "model_revision": "main", "model_sha": "13afe5124825b4f3751f836b40dafda64c1ed062", "batch_size": "auto", "batch_sizes": [], "device": null, "use_cache": null, "limit": null, "bootstrap_iters": 100000, "gen_kwargs": null, "random_seed": 0, "numpy_seed": 1234, "torch_seed": 1234, "fewshot_seed": 1234 }
0b99443
1,734,103,215.14859
PyTorch version: 2.5.1+cu124 Is debug build: False CUDA used to build PyTorch: 12.4 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.3 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.35 Python version: 3.10.11 (main, Apr 17 2023, 17:57:03) [GCC 7.5.0] (64-bit runtime) Python platform: Linux-5.15.0-101-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA H100 80GB HBM3 GPU 1: NVIDIA H100 80GB HBM3 GPU 2: NVIDIA H100 80GB HBM3 GPU 3: NVIDIA H100 80GB HBM3 Nvidia driver version: 560.35.03 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 52 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 192 On-line CPU(s) list: 0-191 Vendor ID: AuthenticAMD Model name: AMD EPYC 9654 96-Core Processor CPU family: 25 Model: 17 Thread(s) per core: 1 Core(s) per socket: 96 Socket(s): 2 Stepping: 1 Frequency boost: enabled CPU max MHz: 3707.8120 CPU min MHz: 1500.0000 BogoMIPS: 4799.99 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d Virtualization: AMD-V L1d cache: 6 MiB (192 instances) L1i cache: 6 MiB (192 instances) L2 cache: 192 MiB (192 instances) L3 cache: 768 MiB (24 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-95 NUMA node1 CPU(s): 96-191 Vulnerability Gather data sampling: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Mitigation; safe RET Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] numpy==2.2.0 [pip3] torch==2.5.1 [pip3] triton==3.1.0 [conda] Could not collect
4.47.0
null
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128,001
131,072
{ "gsm8k": "7acf08a400c7a97fa29fef2256877679b4ae6b1e0bd5343713ce97aa9046c469" }
hf
meta-llama/Llama-3.2-3B
meta-llama__Llama-3.2-3B
null
null
null
false
null
null
759,340.222666
764,433.054194
5092.831527994014
{ "gsm8k": { "alias": "gsm8k", "exact_match,strict-match": 0, "exact_match_stderr,strict-match": 0, "exact_match,flexible-extract": 0.10841546626231995, "exact_match_stderr,flexible-extract": 0.008563852506627495 } }
{ "gsm8k": [] }
{ "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": 0, "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, "top_p": 1 }, "repeats": 1, "filter_list": [ { "name": "strict-match", "filter": [ { "function": "regex", "regex_pattern": "#### (\\-?[0-9\\.\\,]+)", "group_select": null }, { "function": "take_first", "regex_pattern": null, "group_select": null } ] }, { "name": "flexible-extract", "filter": [ { "function": "regex", "regex_pattern": "(-?[$0-9.,]{2,})|(-?[0-9]+)", "group_select": -1 }, { "function": "take_first", "regex_pattern": null, "group_select": null } ] } ], "should_decontaminate": false, "metadata": { "version": 3 } } }
{ "gsm8k": 3 }
{ "gsm8k": 0 }
{ "gsm8k": { "exact_match": true } }
{ "gsm8k": { "original": 1319, "effective": 1319 } }
{ "model": "hf", "model_args": "pretrained=meta-llama/Llama-3.2-3B,dtype=float16", "model_num_parameters": 3212749824, "model_dtype": "torch.float16", "model_revision": "main", "model_sha": "13afe5124825b4f3751f836b40dafda64c1ed062", "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 }
0b99443
1,734,110,658.501217
PyTorch version: 2.5.1+cu124 Is debug build: False CUDA used to build PyTorch: 12.4 ROCM used to build PyTorch: N/A OS: Ubuntu 22.04.3 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.35 Python version: 3.10.11 (main, Apr 17 2023, 17:57:03) [GCC 7.5.0] (64-bit runtime) Python platform: Linux-5.15.0-101-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA H100 80GB HBM3 GPU 1: NVIDIA H100 80GB HBM3 GPU 2: NVIDIA H100 80GB HBM3 GPU 3: NVIDIA H100 80GB HBM3 Nvidia driver version: 560.35.03 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True CPU: Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Address sizes: 52 bits physical, 57 bits virtual Byte Order: Little Endian CPU(s): 192 On-line CPU(s) list: 0-191 Vendor ID: AuthenticAMD Model name: AMD EPYC 9654 96-Core Processor CPU family: 25 Model: 17 Thread(s) per core: 1 Core(s) per socket: 96 Socket(s): 2 Stepping: 1 Frequency boost: enabled CPU max MHz: 3707.8120 CPU min MHz: 1500.0000 BogoMIPS: 4799.99 Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d Virtualization: AMD-V L1d cache: 6 MiB (192 instances) L1i cache: 6 MiB (192 instances) L2 cache: 192 MiB (192 instances) L3 cache: 768 MiB (24 instances) NUMA node(s): 2 NUMA node0 CPU(s): 0-95 NUMA node1 CPU(s): 96-191 Vulnerability Gather data sampling: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Mitigation; safe RET Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected Versions of relevant libraries: [pip3] numpy==2.2.0 [pip3] torch==2.5.1 [pip3] triton==3.1.0 [conda] Could not collect
4.47.0
null
[ "<|end_of_text|>", "128001" ]
[ "<|end_of_text|>", "128001" ]
[ "<|begin_of_text|>", "128000" ]
128,001
131,072
{ "gsm8k": "84d59512bae5be7ab7a8aa6ee025db3460ce8947c48d851de0474f8f29fc2668" }
hf
meta-llama/Llama-3.2-3B
meta-llama__Llama-3.2-3B
null
null
null
false
null
null
766,781.339171
767,712.27869
930.9395187930204

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