File size: 10,933 Bytes
219b2b6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
{
  "results": {
    "leaderboard_musr": {
      " ": " ",
      "alias": "leaderboard_musr"
    },
    "leaderboard_musr_murder_mysteries": {
      "alias": " - leaderboard_musr_murder_mysteries",
      "acc_norm,none": 0.54,
      "acc_norm_stderr,none": 0.03158465389149902
    },
    "leaderboard_musr_object_placements": {
      "alias": " - leaderboard_musr_object_placements",
      "acc_norm,none": 0.234375,
      "acc_norm_stderr,none": 0.02652733398834892
    },
    "leaderboard_musr_team_allocation": {
      "alias": " - leaderboard_musr_team_allocation",
      "acc_norm,none": 0.32,
      "acc_norm_stderr,none": 0.029561724955241033
    }
  },
  "group_subtasks": {
    "leaderboard_musr": [
      "leaderboard_musr_murder_mysteries",
      "leaderboard_musr_object_placements",
      "leaderboard_musr_team_allocation"
    ]
  },
  "configs": {
    "leaderboard_musr_murder_mysteries": {
      "task": "leaderboard_musr_murder_mysteries",
      "dataset_path": "TAUR-Lab/MuSR",
      "test_split": "murder_mysteries",
      "doc_to_text": "def doc_to_text(doc):\n    \"\"\"\n    Convert a doc to text.\n    \"\"\"\n    choices = \"\"\n    for i, choice in enumerate(ast.literal_eval(doc[\"choices\"])):\n        choices += f\"{i+1} - {choice}\\n\"\n\n    text = DOC_TO_TEXT.format(\n        narrative=doc[\"narrative\"], question=doc[\"question\"], choices=choices\n    )\n\n    return text\n",
      "doc_to_target": "{{answer_choice}}",
      "doc_to_choice": "{{choices}}",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 0,
      "metric_list": [
        {
          "metric": "acc_norm",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 1.0
      }
    },
    "leaderboard_musr_object_placements": {
      "task": "leaderboard_musr_object_placements",
      "dataset_path": "TAUR-Lab/MuSR",
      "test_split": "object_placements",
      "doc_to_text": "def doc_to_text(doc):\n    \"\"\"\n    Convert a doc to text.\n    \"\"\"\n    choices = \"\"\n    for i, choice in enumerate(ast.literal_eval(doc[\"choices\"])):\n        choices += f\"{i+1} - {choice}\\n\"\n\n    text = DOC_TO_TEXT.format(\n        narrative=doc[\"narrative\"], question=doc[\"question\"], choices=choices\n    )\n\n    return text\n",
      "doc_to_target": "{{answer_choice}}",
      "doc_to_choice": "{{choices}}",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 0,
      "metric_list": [
        {
          "metric": "acc_norm",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 1.0
      }
    },
    "leaderboard_musr_team_allocation": {
      "task": "leaderboard_musr_team_allocation",
      "dataset_path": "TAUR-Lab/MuSR",
      "test_split": "team_allocation",
      "doc_to_text": "def doc_to_text(doc):\n    \"\"\"\n    Convert a doc to text.\n    \"\"\"\n    choices = \"\"\n    for i, choice in enumerate(ast.literal_eval(doc[\"choices\"])):\n        choices += f\"{i+1} - {choice}\\n\"\n\n    text = DOC_TO_TEXT.format(\n        narrative=doc[\"narrative\"], question=doc[\"question\"], choices=choices\n    )\n\n    return text\n",
      "doc_to_target": "{{answer_choice}}",
      "doc_to_choice": "{{choices}}",
      "description": "",
      "target_delimiter": " ",
      "fewshot_delimiter": "\n\n",
      "num_fewshot": 0,
      "metric_list": [
        {
          "metric": "acc_norm",
          "aggregation": "mean",
          "higher_is_better": true
        }
      ],
      "output_type": "multiple_choice",
      "repeats": 1,
      "should_decontaminate": false,
      "metadata": {
        "version": 1.0
      }
    }
  },
  "versions": {
    "leaderboard_musr_murder_mysteries": 1.0,
    "leaderboard_musr_object_placements": 1.0,
    "leaderboard_musr_team_allocation": 1.0
  },
  "n-shot": {
    "leaderboard_musr_murder_mysteries": 0,
    "leaderboard_musr_object_placements": 0,
    "leaderboard_musr_team_allocation": 0
  },
  "higher_is_better": {
    "leaderboard_musr": {
      "acc_norm": true
    },
    "leaderboard_musr_murder_mysteries": {
      "acc_norm": true
    },
    "leaderboard_musr_object_placements": {
      "acc_norm": true
    },
    "leaderboard_musr_team_allocation": {
      "acc_norm": true
    }
  },
  "n-samples": {
    "leaderboard_musr_murder_mysteries": {
      "original": 250,
      "effective": 250
    },
    "leaderboard_musr_object_placements": {
      "original": 256,
      "effective": 256
    },
    "leaderboard_musr_team_allocation": {
      "original": 250,
      "effective": 250
    }
  },
  "config": {
    "model": "hf",
    "model_args": "pretrained=DevQuasar/analytical_reasoning_r16a32_unsloth-Llama-3.2-3B-Instruct-bnb-4bit",
    "batch_size": "auto:4",
    "batch_sizes": [
      16,
      16,
      16,
      32
    ],
    "device": null,
    "use_cache": "eval_cache",
    "limit": null,
    "bootstrap_iters": 100000,
    "gen_kwargs": null,
    "random_seed": 0,
    "numpy_seed": 1234,
    "torch_seed": 1234,
    "fewshot_seed": 1234
  },
  "git_hash": "0230356",
  "date": 1732986471.4917576,
  "pretty_env_info": "PyTorch version: 2.5.1+cu124\nIs debug build: False\nCUDA used to build PyTorch: 12.4\nROCM used to build PyTorch: N/A\n\nOS: Debian GNU/Linux 12 (bookworm) (x86_64)\nGCC version: (Debian 12.2.0-14) 12.2.0\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.36\n\nPython version: 3.11.10 (main, Oct  3 2024, 07:29:13) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-6.1.0-26-amd64-x86_64-with-glibc2.36\nIs CUDA available: True\nCUDA runtime version: Could not collect\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA GeForce GTX 1050 Ti\nGPU 1: Tesla P40\nGPU 2: Tesla V100-PCIE-32GB\nGPU 3: Tesla V100-PCIE-32GB\n\nNvidia driver version: 535.183.01\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\nAddress sizes:                        43 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 1950X 16-Core Processor\nCPU family:                           23\nModel:                                1\nThread(s) per core:                   2\nCore(s) per socket:                   16\nSocket(s):                            1\nStepping:                             1\nFrequency boost:                      enabled\nCPU(s) scaling MHz:                   66%\nCPU max MHz:                          3400.0000\nCPU min MHz:                          2200.0000\nBogoMIPS:                             6786.43\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 amd_dcm aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sev\nVirtualization:                       AMD-V\nL1d cache:                            512 KiB (16 instances)\nL1i cache:                            1 MiB (16 instances)\nL2 cache:                             8 MiB (16 instances)\nL3 cache:                             32 MiB (4 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:               Mitigation; untrained return thunk; SMT vulnerable\nVulnerability Spec rstack overflow:   Mitigation; safe RET\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; 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==2.1.3\n[pip3] torch==2.5.1\n[pip3] triton==3.1.0\n[conda] numpy                     2.1.3                    pypi_0    pypi\n[conda] torch                     2.5.1                    pypi_0    pypi\n[conda] triton                    3.1.0                    pypi_0    pypi",
  "transformers_version": "4.46.3",
  "upper_git_hash": null,
  "tokenizer_pad_token": [
    "<|finetune_right_pad_id|>",
    "128004"
  ],
  "tokenizer_eos_token": [
    "<|eot_id|>",
    "128009"
  ],
  "tokenizer_bos_token": [
    "<|begin_of_text|>",
    "128000"
  ],
  "eot_token_id": 128009,
  "max_length": 131072,
  "task_hashes": {
    "leaderboard_musr_murder_mysteries": "a696259562ea5c5c09a2613e30526fae1de29f55da9e28e8d7e8a53027e6d330",
    "leaderboard_musr_object_placements": "3aa8c5e5bc59cd6ba2326269b9f0bf3cee8cba1b4e9e1d1330cf5f1f59ea0dce",
    "leaderboard_musr_team_allocation": "5a75f135c145ee861a1cf31b63346709ef41b9d542be6a61c5818c210a3797a5"
  },
  "model_source": "hf",
  "model_name": "DevQuasar/analytical_reasoning_r16a32_unsloth-Llama-3.2-3B-Instruct-bnb-4bit",
  "model_name_sanitized": "DevQuasar__analytical_reasoning_r16a32_unsloth-Llama-3.2-3B-Instruct-bnb-4bit",
  "system_instruction": null,
  "system_instruction_sha": null,
  "fewshot_as_multiturn": false,
  "chat_template": null,
  "chat_template_sha": null,
  "start_time": 52195.45405349,
  "end_time": 52407.302247922,
  "total_evaluation_time_seconds": "211.84819443200104"
}