Delete model_data/external/vikhr-53-32k-7bapsys.json

model_data/external/vikhr-53-32k-7bapsys.json

@@ -1,233 +0,0 @@
-{
-  "results": {
-    "musicmc": {
-      "acc,none": 0.24468085106382978,
-      "acc_stderr,none": 0.019850836731679165,
-      "alias": "musicmc"
-    },
-    "moviemc": {
-      "acc,none": 0.4050925925925926,
-      "acc_stderr,none": 0.02364630048710984,
-      "alias": "moviemc"
-    },
-    "lawmc": {
-      "acc,none": 0.4788877445932029,
-      "acc_stderr,none": 0.016039714649993996,
-      "alias": "lawmc"
-    },
-    "bookmc": {
-      "acc,none": 0.3049792531120332,
-      "acc_stderr,none": 0.02099237106666469,
-      "alias": "bookmc"
-    }
-  },
-  "group_subtasks": {
-    "bookmc": [],
-    "lawmc": [],
-    "moviemc": [],
-    "musicmc": []
-  },
-  "configs": {
-    "bookmc": {
-      "task": "bookmc",
-      "dataset_path": "Vikhrmodels/books_mc",
-      "training_split": "train",
-      "validation_split": "train",
-      "process_docs": "def process_docs(dataset: Dataset):\n    def _helper(doc,index):\n        field = doc['correct_answer']\n        fi = None\n        if field == 'answerA' or field in ['A','А']:\n            fi = \"A\"\n        if field == 'answerB' or field in ['B','Б']:\n            fi = \"B\"\n        if field == 'answerC' or field in ['C','С']:\n            fi = \"C\"\n        if field == 'answerD' or field in ['D','Д']:\n            fi = \"D\"\n        if fi:\n            doc[\"choices\"] = [doc[f\"answer{s}\"] for s in list('ABCD')]\n\n        else:\n            for idx,(key,val) in enumerate(list(doc.items())[1:-1]):\n                if field == val:\n                    fi = {0:\"A\",1:\"B\",2:\"C\",3:\"D\"}.get(idx)\n\n\n        doc[\"choices\"] = [doc[f\"answer{s}\"] for s in list('ABCD')]\n        if index == 0:\n            next_doc1 = dataset[index + 1]\n            next_doc2 = dataset[index + 2] if index + 2 < len(dataset) else None\n            doc[\"choices\"].extend([next_doc1[f\"answer{s}\"] for s in list('ABCD')])\n            doc[\"choices\"].extend([next_doc2[f\"answer{s}\"] for s in list('ABCD')])\n        elif index == len(dataset) - 1:\n            prev_doc1 = dataset[index - 1]\n            prev_doc2 = dataset[index - 2]\n            doc[\"choices\"].extend([prev_doc1[f\"answer{s}\"] for s in list('ABCD')])\n            doc[\"choices\"].extend([prev_doc2[f\"answer{s}\"] for s in list('ABCD')])\n        else:\n            prev_doc = dataset[index - 1]\n            next_doc = dataset[index + 1]\n            doc[\"choices\"].extend([prev_doc[f\"answer{s}\"] for s in list('ABCD')])\n            doc[\"choices\"].extend([next_doc[f\"answer{s}\"] for s in list('ABCD')])\n\n        label_map = {label: i for i, label in enumerate(string.ascii_uppercase[:12])}\n        inv_label_map = {i: label for i, label in enumerate(string.ascii_uppercase[:12])}\n        # print(label_map)\n        correct_label = label_map.get(fi)\n        if not correct_label: return {\"label\":\"failed row w/o answer\",\"gold\":\"\"}\n\n        random.shuffle(doc[\"choices\"])\n        # print(f\"answer{list('ABCD')[fi]}\",doc[f\"answer{list('ABCD')[fi]}\"])\n        gold = fi\n        shuffled_label = doc[\"choices\"].index(doc[f\"answer{gold}\"])\n        doc[\"label\"] = inv_label_map[shuffled_label]\n        doc[\"gold\"] = inv_label_map[shuffled_label]\n        # print(doc)\n\n\n        return doc\n    ds = dataset.map(_helper,with_indices=True)\n    return ds.filter(lambda x: len(x['gold'])==1)\n",
-      "doc_to_text": "{{question}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nE. {{choices[4]}}\nF. {{choices[5]}}\nG. {{choices[6]}}\nH. {{choices[7]}}\nK. {{choices[8]}}\nI. {{choices[9]}}\nJ. {{choices[10]}}\nL. {{choices[11]}}\nОтвет:",
-      "doc_to_target": "label",
-      "doc_to_choice": [
-        "A",
-        "B",
-        "C",
-        "D",
-        "E",
-        "F",
-        "G",
-        "H",
-        "I",
-        "J",
-        "K",
-        "L"
-      ],
-      "description": "",
-      "target_delimiter": " ",
-      "fewshot_delimiter": "\n\n",
-      "num_fewshot": 0,
-      "metric_list": [
-        {
-          "metric": "acc"
-        }
-      ],
-      "output_type": "multiple_choice",
-      "repeats": 1,
-      "should_decontaminate": false
-    },
-    "lawmc": {
-      "task": "lawmc",
-      "dataset_path": "Vikhrmodels/law_mc",
-      "training_split": "train",
-      "validation_split": "train",
-      "process_docs": "def process_docs(dataset: Dataset):\n    def _helper(doc,index):\n        field = doc['correct_answer']\n        fi = None\n        if field == 'answerA' or field in ['A','А']:\n            fi = \"A\"\n        if field == 'answerB' or field in ['B','Б']:\n            fi = \"B\"\n        if field == 'answerC' or field in ['C','С']:\n            fi = \"C\"\n        if field == 'answerD' or field in ['D','Д']:\n            fi = \"D\"\n        if fi:\n            doc[\"choices\"] = [doc[f\"answer{s}\"] for s in list('ABCD')]\n\n        else:\n            for idx,(key,val) in enumerate(list(doc.items())[1:-1]):\n                if field == val:\n                    fi = {0:\"A\",1:\"B\",2:\"C\",3:\"D\"}.get(idx)\n\n\n        doc[\"choices\"] = [doc[f\"answer{s}\"] for s in list('ABCD')]\n        if index == 0:\n            next_doc1 = dataset[index + 1]\n            next_doc2 = dataset[index + 2] if index + 2 < len(dataset) else None\n            doc[\"choices\"].extend([next_doc1[f\"answer{s}\"] for s in list('ABCD')])\n            doc[\"choices\"].extend([next_doc2[f\"answer{s}\"] for s in list('ABCD')])\n        elif index == len(dataset) - 1:\n            prev_doc1 = dataset[index - 1]\n            prev_doc2 = dataset[index - 2]\n            doc[\"choices\"].extend([prev_doc1[f\"answer{s}\"] for s in list('ABCD')])\n            doc[\"choices\"].extend([prev_doc2[f\"answer{s}\"] for s in list('ABCD')])\n        else:\n            prev_doc = dataset[index - 1]\n            next_doc = dataset[index + 1]\n            doc[\"choices\"].extend([prev_doc[f\"answer{s}\"] for s in list('ABCD')])\n            doc[\"choices\"].extend([next_doc[f\"answer{s}\"] for s in list('ABCD')])\n\n        label_map = {label: i for i, label in enumerate(string.ascii_uppercase[:12])}\n        inv_label_map = {i: label for i, label in enumerate(string.ascii_uppercase[:12])}\n        # print(label_map)\n        correct_label = label_map.get(fi)\n        if not correct_label: return {\"label\":\"failed row w/o answer\",\"gold\":\"\"}\n\n        random.shuffle(doc[\"choices\"])\n        # print(f\"answer{list('ABCD')[fi]}\",doc[f\"answer{list('ABCD')[fi]}\"])\n        gold = fi\n        shuffled_label = doc[\"choices\"].index(doc[f\"answer{gold}\"])\n        doc[\"label\"] = inv_label_map[shuffled_label]\n        doc[\"gold\"] = inv_label_map[shuffled_label]\n        # print(doc)\n\n\n        return doc\n    ds = dataset.map(_helper,with_indices=True)\n    return ds.filter(lambda x: len(x['gold'])==1)\n",
-      "doc_to_text": "{{question}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nE. {{choices[4]}}\nF. {{choices[5]}}\nG. {{choices[6]}}\nH. {{choices[7]}}\nK. {{choices[8]}}\nI. {{choices[9]}}\nJ. {{choices[10]}}\nL. {{choices[11]}}\nОтвет:",
-      "doc_to_target": "label",
-      "doc_to_choice": [
-        "A",
-        "B",
-        "C",
-        "D",
-        "E",
-        "F",
-        "G",
-        "H",
-        "I",
-        "J",
-        "K",
-        "L"
-      ],
-      "description": "",
-      "target_delimiter": " ",
-      "fewshot_delimiter": "\n\n",
-      "num_fewshot": 0,
-      "metric_list": [
-        {
-          "metric": "acc"
-        }
-      ],
-      "output_type": "multiple_choice",
-      "repeats": 1,
-      "should_decontaminate": false
-    },
-    "moviemc": {
-      "task": "moviemc",
-      "dataset_path": "Vikhrmodels/movie_mc",
-      "training_split": "train",
-      "validation_split": "train",
-      "process_docs": "def process_docs(dataset: Dataset):\n    def _helper(doc,index):\n        field = doc['correct_answer']\n        fi = None\n        if field == 'answerA' or field in ['A','А']:\n            fi = \"A\"\n        if field == 'answerB' or field in ['B','Б']:\n            fi = \"B\"\n        if field == 'answerC' or field in ['C','С']:\n            fi = \"C\"\n        if field == 'answerD' or field in ['D','Д']:\n            fi = \"D\"\n        if fi:\n            doc[\"choices\"] = [doc[f\"answer{s}\"] for s in list('ABCD')]\n\n        else:\n            for idx,(key,val) in enumerate(list(doc.items())[1:-1]):\n                if field == val:\n                    fi = {0:\"A\",1:\"B\",2:\"C\",3:\"D\"}.get(idx)\n\n\n        doc[\"choices\"] = [doc[f\"answer{s}\"] for s in list('ABCD')]\n        if index == 0:\n            next_doc1 = dataset[index + 1]\n            next_doc2 = dataset[index + 2] if index + 2 < len(dataset) else None\n            doc[\"choices\"].extend([next_doc1[f\"answer{s}\"] for s in list('ABCD')])\n            doc[\"choices\"].extend([next_doc2[f\"answer{s}\"] for s in list('ABCD')])\n        elif index == len(dataset) - 1:\n            prev_doc1 = dataset[index - 1]\n            prev_doc2 = dataset[index - 2]\n            doc[\"choices\"].extend([prev_doc1[f\"answer{s}\"] for s in list('ABCD')])\n            doc[\"choices\"].extend([prev_doc2[f\"answer{s}\"] for s in list('ABCD')])\n        else:\n            prev_doc = dataset[index - 1]\n            next_doc = dataset[index + 1]\n            doc[\"choices\"].extend([prev_doc[f\"answer{s}\"] for s in list('ABCD')])\n            doc[\"choices\"].extend([next_doc[f\"answer{s}\"] for s in list('ABCD')])\n\n        label_map = {label: i for i, label in enumerate(string.ascii_uppercase[:12])}\n        inv_label_map = {i: label for i, label in enumerate(string.ascii_uppercase[:12])}\n        # print(label_map)\n        correct_label = label_map.get(fi)\n        if not correct_label: return {\"label\":\"failed row w/o answer\",\"gold\":\"\"}\n\n        random.shuffle(doc[\"choices\"])\n        # print(f\"answer{list('ABCD')[fi]}\",doc[f\"answer{list('ABCD')[fi]}\"])\n        gold = fi\n        shuffled_label = doc[\"choices\"].index(doc[f\"answer{gold}\"])\n        doc[\"label\"] = inv_label_map[shuffled_label]\n        doc[\"gold\"] = inv_label_map[shuffled_label]\n        # print(doc)\n\n\n        return doc\n    ds = dataset.map(_helper,with_indices=True)\n    return ds.filter(lambda x: len(x['gold'])==1)\n",
-      "doc_to_text": "{{question}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nE. {{choices[4]}}\nF. {{choices[5]}}\nG. {{choices[6]}}\nH. {{choices[7]}}\nK. {{choices[8]}}\nI. {{choices[9]}}\nJ. {{choices[10]}}\nL. {{choices[11]}}\nОтвет:",
-      "doc_to_target": "label",
-      "doc_to_choice": [
-        "A",
-        "B",
-        "C",
-        "D",
-        "E",
-        "F",
-        "G",
-        "H",
-        "I",
-        "J",
-        "K",
-        "L"
-      ],
-      "description": "",
-      "target_delimiter": " ",
-      "fewshot_delimiter": "\n\n",
-      "num_fewshot": 0,
-      "metric_list": [
-        {
-          "metric": "acc"
-        }
-      ],
-      "output_type": "multiple_choice",
-      "repeats": 1,
-      "should_decontaminate": false
-    },
-    "musicmc": {
-      "task": "musicmc",
-      "dataset_path": "Vikhrmodels/music_mc",
-      "training_split": "train",
-      "validation_split": "train",
-      "process_docs": "def process_docs(dataset: Dataset):\n    def _helper(doc,index):\n        field = doc['correct_answer']\n        fi = None\n        if field == 'answerA' or field in ['A','А']:\n            fi = \"A\"\n        if field == 'answerB' or field in ['B','Б']:\n            fi = \"B\"\n        if field == 'answerC' or field in ['C','С']:\n            fi = \"C\"\n        if field == 'answerD' or field in ['D','Д']:\n            fi = \"D\"\n        if fi:\n            doc[\"choices\"] = [doc[f\"answer{s}\"] for s in list('ABCD')]\n\n        else:\n            for idx,(key,val) in enumerate(list(doc.items())[1:-1]):\n                if field == val:\n                    fi = {0:\"A\",1:\"B\",2:\"C\",3:\"D\"}.get(idx)\n\n\n        doc[\"choices\"] = [doc[f\"answer{s}\"] for s in list('ABCD')]\n        if index == 0:\n            next_doc1 = dataset[index + 1]\n            next_doc2 = dataset[index + 2] if index + 2 < len(dataset) else None\n            doc[\"choices\"].extend([next_doc1[f\"answer{s}\"] for s in list('ABCD')])\n            doc[\"choices\"].extend([next_doc2[f\"answer{s}\"] for s in list('ABCD')])\n        elif index == len(dataset) - 1:\n            prev_doc1 = dataset[index - 1]\n            prev_doc2 = dataset[index - 2]\n            doc[\"choices\"].extend([prev_doc1[f\"answer{s}\"] for s in list('ABCD')])\n            doc[\"choices\"].extend([prev_doc2[f\"answer{s}\"] for s in list('ABCD')])\n        else:\n            prev_doc = dataset[index - 1]\n            next_doc = dataset[index + 1]\n            doc[\"choices\"].extend([prev_doc[f\"answer{s}\"] for s in list('ABCD')])\n            doc[\"choices\"].extend([next_doc[f\"answer{s}\"] for s in list('ABCD')])\n\n        label_map = {label: i for i, label in enumerate(string.ascii_uppercase[:12])}\n        inv_label_map = {i: label for i, label in enumerate(string.ascii_uppercase[:12])}\n        # print(label_map)\n        correct_label = label_map.get(fi)\n        if not correct_label: return {\"label\":\"failed row w/o answer\",\"gold\":\"\"}\n\n        random.shuffle(doc[\"choices\"])\n        # print(f\"answer{list('ABCD')[fi]}\",doc[f\"answer{list('ABCD')[fi]}\"])\n        gold = fi\n        shuffled_label = doc[\"choices\"].index(doc[f\"answer{gold}\"])\n        doc[\"label\"] = inv_label_map[shuffled_label]\n        doc[\"gold\"] = inv_label_map[shuffled_label]\n        # print(doc)\n\n\n        return doc\n    ds = dataset.map(_helper,with_indices=True)\n    return ds.filter(lambda x: len(x['gold'])==1)\n",
-      "doc_to_text": "{{question}}\nA. {{choices[0]}}\nB. {{choices[1]}}\nC. {{choices[2]}}\nD. {{choices[3]}}\nE. {{choices[4]}}\nF. {{choices[5]}}\nG. {{choices[6]}}\nH. {{choices[7]}}\nK. {{choices[8]}}\nI. {{choices[9]}}\nJ. {{choices[10]}}\nL. {{choices[11]}}\nОтвет:",
-      "doc_to_target": "label",
-      "doc_to_choice": [
-        "A",
-        "B",
-        "C",
-        "D",
-        "E",
-        "F",
-        "G",
-        "H",
-        "I",
-        "J",
-        "K",
-        "L"
-      ],
-      "description": "",
-      "target_delimiter": " ",
-      "fewshot_delimiter": "\n\n",
-      "num_fewshot": 0,
-      "metric_list": [
-        {
-          "metric": "acc"
-        }
-      ],
-      "output_type": "multiple_choice",
-      "repeats": 1,
-      "should_decontaminate": false
-    }
-  },
-  "versions": {
-    "bookmc": "Yaml",
-    "lawmc": "Yaml",
-    "moviemc": "Yaml",
-    "musicmc": "Yaml"
-  },
-  "n-shot": {
-    "bookmc": 0,
-    "lawmc": 0,
-    "moviemc": 0,
-    "musicmc": 0
-  },
-  "n-samples": {
-    "musicmc": {
-      "original": 470,
-      "effective": 470
-    },
-    "moviemc": {
-      "original": 432,
-      "effective": 432
-    },
-    "lawmc": {
-      "original": 971,
-      "effective": 971
-    },
-    "bookmc": {
-      "original": 482,
-      "effective": 482
-    }
-  },
-  "config": {
-    "model": "hf",
-    "model_args": "pretrained=Vikhrmodels/it-5.3-fp16-32k,dtype=float16",
-    "model_num_parameters": 7627550720,
-    "model_dtype": "torch.float16",
-    "model_revision": "main",
-    "model_sha": "9efc89dcaf89fd094a909a3d078890a5ab74542b",
-    "batch_size": "10",
-    "batch_sizes": [],
-    "device": "3",
-    "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": null,
-  "date": 1717619450.4005609,
-  "pretty_env_info": "PyTorch version: 2.3.0+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 20.04.6 LTS (x86_64)\nGCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0\nClang version: Could not collect\nCMake version: version 3.27.0\nLibc version: glibc-2.31\n\nPython version: 3.8.10 (default, May 26 2023, 14:05:08)  [GCC 9.4.0] (64-bit runtime)\nPython platform: Linux-5.4.0-150-generic-x86_64-with-glibc2.29\nIs CUDA available: True\nCUDA runtime version: 10.1.243\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: Tesla V100-SXM2-32GB\nGPU 1: Tesla V100-SXM2-32GB\nGPU 2: Tesla V100-SXM2-32GB\nGPU 3: Tesla V100-SXM2-32GB\n\nNvidia driver version: 525.85.12\ncuDNN version: Could not collect\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture:                    x86_64\nCPU op-mode(s):                  32-bit, 64-bit\nByte Order:                      Little Endian\nAddress sizes:                   46 bits physical, 48 bits virtual\nCPU(s):                          48\nOn-line CPU(s) list:             0-47\nThread(s) per core:              2\nCore(s) per socket:              12\nSocket(s):                       2\nNUMA node(s):                    2\nVendor ID:                       GenuineIntel\nCPU family:                      6\nModel:                           63\nModel name:                      Intel(R) Xeon(R) CPU E5-2680 v3 @ 2.50GHz\nStepping:                        2\nCPU MHz:                         1200.181\nCPU max MHz:                     3300.0000\nCPU min MHz:                     1200.0000\nBogoMIPS:                        5000.01\nVirtualization:                  VT-x\nL1d cache:                       768 KiB\nL1i cache:                       768 KiB\nL2 cache:                        6 MiB\nL3 cache:                        60 MiB\nNUMA node0 CPU(s):               0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46\nNUMA node1 CPU(s):               1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47\nVulnerability Itlb multihit:     KVM: Mitigation: Split huge pages\nVulnerability L1tf:              Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable\nVulnerability Mds:               Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Meltdown:          Mitigation; PTI\nVulnerability Mmio stale data:   Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Retbleed:          Not affected\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, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds:             Not affected\nVulnerability Tsx async abort:   Not affected\nFlags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm cpuid_fault epb invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm xsaveopt cqm_llc cqm_occup_llc dtherm ida arat pln pts md_clear flush_l1d\n\nVersions of relevant libraries:\n[pip3] efficientnet-pytorch==0.6.3\n[pip3] ema-pytorch==0.3.1\n[pip3] magvit2-pytorch==0.2.0\n[pip3] mypy-extensions==1.0.0\n[pip3] numpy==1.24.4\n[pip3] onnx==1.15.0\n[pip3] open-clip-torch==2.20.0\n[pip3] pytorch-ignite==0.5.0.post2\n[pip3] pytorch-lightning==2.0.1\n[pip3] pytorch-metric-learning==2.1.2\n[pip3] pytorch-msssim==1.0.0\n[pip3] pytorch-ranger==0.1.1\n[pip3] pytorch-warmup==0.1.1\n[pip3] rotary-embedding-torch==0.5.2\n[pip3] segmentation-models-pytorch==0.1.3\n[pip3] torch==2.3.0\n[pip3] torch-optimizer==0.3.0\n[pip3] torchaudio==2.0.0\n[pip3] torchdata==0.6.0\n[pip3] torchmetrics==1.3.0.post0\n[pip3] torchsde==0.2.6\n[pip3] torchvision==0.17.2\n[pip3] triton==2.3.0\n[pip3] vector-quantize-pytorch==1.12.5\n[conda] Could not collect",
-  "transformers_version": "4.41.1",
-  "upper_git_hash": null,
-  "task_hashes": {},
-  "model_source": "hf",
-  "model_name": "Vikhrmodels/it-5.3-fp16-32k",
-  "model_name_sanitized": "Vikhrmodels__it-5.3-fp16-32k",
-  "start_time": 8471655.240029689,
-  "end_time": 8471809.526610821,
-  "total_evaluation_time_seconds": "154.28658113256097"
-}