| { |
| "results": { |
| "gsm8k": { |
| "alias": "gsm8k", |
| "exact_match,strict-match": 0.34950720242608035, |
| "exact_match_stderr,strict-match": 0.013133836511705991, |
| "exact_match,flexible-extract": 0.3555724033358605, |
| "exact_match_stderr,flexible-extract": 0.013185402252713849 |
| } |
| }, |
| "group_subtasks": { |
| "gsm8k": [] |
| }, |
| "configs": { |
| "gsm8k": { |
| "task": "gsm8k", |
| "tag": [ |
| "math_word_problems" |
| ], |
| "dataset_path": "gsm8k", |
| "dataset_name": "main", |
| "training_split": "train", |
| "test_split": "test", |
| "fewshot_split": "train", |
| "doc_to_text": "Question: {{question}}\nAnswer:", |
| "doc_to_target": "{{answer}}", |
| "unsafe_code": false, |
| "description": "", |
| "target_delimiter": " ", |
| "fewshot_delimiter": "\n\n", |
| "num_fewshot": 5, |
| "metric_list": [ |
| { |
| "metric": "exact_match", |
| "aggregation": "mean", |
| "higher_is_better": true, |
| "ignore_case": true, |
| "ignore_punctuation": false, |
| "regexes_to_ignore": [ |
| ",", |
| "\\$", |
| "(?s).*#### ", |
| "\\.$" |
| ] |
| } |
| ], |
| "output_type": "generate_until", |
| "generation_kwargs": { |
| "until": [ |
| "Question:", |
| "</s>", |
| "<|im_end|>" |
| ], |
| "do_sample": false, |
| "temperature": 0.0 |
| }, |
| "repeats": 1, |
| "filter_list": [ |
| { |
| "name": "strict-match", |
| "filter": [ |
| { |
| "function": "regex", |
| "regex_pattern": "#### (\\-?[0-9\\.\\,]+)" |
| }, |
| { |
| "function": "take_first" |
| } |
| ] |
| }, |
| { |
| "name": "flexible-extract", |
| "filter": [ |
| { |
| "function": "regex", |
| "group_select": -1, |
| "regex_pattern": "(-?[$0-9.,]{2,})|(-?[0-9]+)" |
| }, |
| { |
| "function": "take_first" |
| } |
| ] |
| } |
| ], |
| "should_decontaminate": false, |
| "metadata": { |
| "version": 3.0, |
| "pretrained": "/home/dongwook/task/allganize/huggingface_cache/dongwookkwon/qwen0.5b-tech-interview-test", |
| "trust_remote_code": true, |
| "dtype": "float16", |
| "gpu_memory_utilization": 0.5 |
| } |
| } |
| }, |
| "versions": { |
| "gsm8k": 3.0 |
| }, |
| "n-shot": { |
| "gsm8k": 5 |
| }, |
| "higher_is_better": { |
| "gsm8k": { |
| "exact_match": true |
| } |
| }, |
| "n-samples": { |
| "gsm8k": { |
| "original": 1319, |
| "effective": 1319 |
| } |
| }, |
| "config": { |
| "model": "vllm", |
| "model_args": "pretrained=/home/dongwook/task/allganize/huggingface_cache/dongwookkwon/qwen0.5b-tech-interview-test,trust_remote_code=True,dtype=float16,gpu_memory_utilization=0.5", |
| "batch_size": "auto:4", |
| "batch_sizes": [], |
| "device": "cuda:0", |
| "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": "3c710e0", |
| "date": 1762181122.5749974, |
| "pretty_env_info": "PyTorch version: 2.8.0+cu128\nIs debug build: False\nCUDA used to build PyTorch: 12.8\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.5 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0\nClang version: Could not collect\nCMake version: version 3.22.1\nLibc version: glibc-2.35\n\nPython version: 3.10.19 | packaged by conda-forge | (main, Oct 22 2025, 22:29:10) [GCC 14.3.0] (64-bit runtime)\nPython platform: Linux-6.8.0-79-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 11.5.119\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: GPU 0: NVIDIA RTX 6000 Ada Generation\nNvidia driver version: 580.82.07\ncuDNN version: Probably one of the following:\n/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn.so.8.9.7\n/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.9.7\n/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.9.7\n/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.9.7\n/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.9.7\n/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.9.7\n/usr/local/cuda-12.1/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.9.7\n/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn.so.8.9.7\n/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.9.7\n/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.9.7\n/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.9.7\n/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.9.7\n/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.9.7\n/usr/local/cuda-12.2/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.9.7\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): 32\nOn-line CPU(s) list: 0-31\nVendor ID: AuthenticAMD\nModel name: AMD Ryzen Threadripper PRO 5955WX 16-Cores\nCPU family: 25\nModel: 8\nThread(s) per core: 2\nCore(s) per socket: 16\nSocket(s): 1\nStepping: 2\nFrequency boost: enabled\nCPU max MHz: 4000.0000\nCPU min MHz: 1800.0000\nBogoMIPS: 7985.05\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin brs arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm debug_swap\nVirtualization: AMD-V\nL1d cache: 512 KiB (16 instances)\nL1i cache: 512 KiB (16 instances)\nL2 cache: 8 MiB (16 instances)\nL3 cache: 64 MiB (2 instances)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-31\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cufft-cu12==11.3.3.83\n[pip3] nvidia-curand-cu12==10.3.9.90\n[pip3] nvidia-cusolver-cu12==11.7.3.90\n[pip3] nvidia-cusparse-cu12==12.5.8.93\n[pip3] nvidia-cusparselt-cu12==0.7.1\n[pip3] nvidia-nccl-cu12==2.27.3\n[pip3] nvidia-nvjitlink-cu12==12.8.93\n[pip3] nvidia-nvtx-cu12==12.8.90\n[pip3] torch==2.8.0\n[pip3] torchaudio==2.8.0\n[pip3] torchvision==0.23.0\n[pip3] triton==3.4.0\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.8.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.8.93 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.10.2.21 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.3.3.83 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.9.90 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.7.3.90 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.5.8.93 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.7.1 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.27.3 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.8.93 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.8.90 pypi_0 pypi\n[conda] torch 2.8.0 pypi_0 pypi\n[conda] torchaudio 2.8.0 pypi_0 pypi\n[conda] torchvision 0.23.0 pypi_0 pypi\n[conda] triton 3.4.0 pypi_0 pypi", |
| "transformers_version": "4.57.1", |
| "lm_eval_version": "0.4.9.1", |
| "upper_git_hash": null, |
| "tokenizer_pad_token": [ |
| "<|endoftext|>", |
| "151643" |
| ], |
| "tokenizer_eos_token": [ |
| "<|im_end|>", |
| "151645" |
| ], |
| "tokenizer_bos_token": [ |
| null, |
| "None" |
| ], |
| "eot_token_id": 151645, |
| "max_length": 32768, |
| "task_hashes": {}, |
| "model_source": "vllm", |
| "model_name": "/home/dongwook/task/allganize/huggingface_cache/dongwookkwon/qwen0.5b-tech-interview-test", |
| "model_name_sanitized": "__home__dongwook__task__allganize__huggingface_cache__dongwookkwon__qwen0.5b-tech-interview-test", |
| "system_instruction": null, |
| "system_instruction_sha": null, |
| "fewshot_as_multiturn": false, |
| "chat_template": null, |
| "chat_template_sha": null, |
| "start_time": 4843057.177305116, |
| "end_time": 4843164.274402437, |
| "total_evaluation_time_seconds": "107.09709732048213" |
| } |
