Upload results for model internlm/internlm2-chat-7b

#364
data/internlm/internlm2-chat-7b/base/24-05-09-04:32:13.json ADDED
@@ -0,0 +1,223 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "results": {
3
+ "ut-voluptate-3770_lsat-rc_base": {
4
+ "acc,none": 0.35687732342007433,
5
+ "acc_stderr,none": 0.029264357329058674,
6
+ "alias": "ut-voluptate-3770_lsat-rc_base"
7
+ },
8
+ "ut-voluptate-3770_lsat-lr_base": {
9
+ "acc,none": 0.26666666666666666,
10
+ "acc_stderr,none": 0.019600907899442414,
11
+ "alias": "ut-voluptate-3770_lsat-lr_base"
12
+ },
13
+ "ut-voluptate-3770_lsat-ar_base": {
14
+ "acc,none": 0.20869565217391303,
15
+ "acc_stderr,none": 0.026854108265439658,
16
+ "alias": "ut-voluptate-3770_lsat-ar_base"
17
+ },
18
+ "ut-voluptate-3770_logiqa_base": {
19
+ "acc,none": 0.31309904153354634,
20
+ "acc_stderr,none": 0.018550171178695687,
21
+ "alias": "ut-voluptate-3770_logiqa_base"
22
+ },
23
+ "ut-voluptate-3770_logiqa2_base": {
24
+ "acc,none": 0.36005089058524176,
25
+ "acc_stderr,none": 0.012110625421739312,
26
+ "alias": "ut-voluptate-3770_logiqa2_base"
27
+ }
28
+ },
29
+ "group_subtasks": {
30
+ "ut-voluptate-3770_logiqa2_base": [],
31
+ "ut-voluptate-3770_logiqa_base": [],
32
+ "ut-voluptate-3770_lsat-ar_base": [],
33
+ "ut-voluptate-3770_lsat-lr_base": [],
34
+ "ut-voluptate-3770_lsat-rc_base": []
35
+ },
36
+ "configs": {
37
+ "ut-voluptate-3770_logiqa2_base": {
38
+ "task": "ut-voluptate-3770_logiqa2_base",
39
+ "group": "logikon-bench",
40
+ "dataset_path": "cot-leaderboard/cot-eval-traces",
41
+ "dataset_kwargs": {
42
+ "data_files": {
43
+ "test": "data/internlm/internlm2-chat-7b/ut-voluptate-3770-logiqa2.parquet"
44
+ }
45
+ },
46
+ "test_split": "test",
47
+ "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
48
+ "doc_to_target": "{{answer}}",
49
+ "doc_to_choice": "{{options}}",
50
+ "description": "",
51
+ "target_delimiter": " ",
52
+ "fewshot_delimiter": "\n\n",
53
+ "num_fewshot": 0,
54
+ "metric_list": [
55
+ {
56
+ "metric": "acc",
57
+ "aggregation": "mean",
58
+ "higher_is_better": true
59
+ }
60
+ ],
61
+ "output_type": "multiple_choice",
62
+ "repeats": 1,
63
+ "should_decontaminate": false,
64
+ "metadata": {
65
+ "version": 0.0
66
+ }
67
+ },
68
+ "ut-voluptate-3770_logiqa_base": {
69
+ "task": "ut-voluptate-3770_logiqa_base",
70
+ "group": "logikon-bench",
71
+ "dataset_path": "cot-leaderboard/cot-eval-traces",
72
+ "dataset_kwargs": {
73
+ "data_files": {
74
+ "test": "data/internlm/internlm2-chat-7b/ut-voluptate-3770-logiqa.parquet"
75
+ }
76
+ },
77
+ "test_split": "test",
78
+ "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
79
+ "doc_to_target": "{{answer}}",
80
+ "doc_to_choice": "{{options}}",
81
+ "description": "",
82
+ "target_delimiter": " ",
83
+ "fewshot_delimiter": "\n\n",
84
+ "num_fewshot": 0,
85
+ "metric_list": [
86
+ {
87
+ "metric": "acc",
88
+ "aggregation": "mean",
89
+ "higher_is_better": true
90
+ }
91
+ ],
92
+ "output_type": "multiple_choice",
93
+ "repeats": 1,
94
+ "should_decontaminate": false,
95
+ "metadata": {
96
+ "version": 0.0
97
+ }
98
+ },
99
+ "ut-voluptate-3770_lsat-ar_base": {
100
+ "task": "ut-voluptate-3770_lsat-ar_base",
101
+ "group": "logikon-bench",
102
+ "dataset_path": "cot-leaderboard/cot-eval-traces",
103
+ "dataset_kwargs": {
104
+ "data_files": {
105
+ "test": "data/internlm/internlm2-chat-7b/ut-voluptate-3770-lsat-ar.parquet"
106
+ }
107
+ },
108
+ "test_split": "test",
109
+ "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
110
+ "doc_to_target": "{{answer}}",
111
+ "doc_to_choice": "{{options}}",
112
+ "description": "",
113
+ "target_delimiter": " ",
114
+ "fewshot_delimiter": "\n\n",
115
+ "num_fewshot": 0,
116
+ "metric_list": [
117
+ {
118
+ "metric": "acc",
119
+ "aggregation": "mean",
120
+ "higher_is_better": true
121
+ }
122
+ ],
123
+ "output_type": "multiple_choice",
124
+ "repeats": 1,
125
+ "should_decontaminate": false,
126
+ "metadata": {
127
+ "version": 0.0
128
+ }
129
+ },
130
+ "ut-voluptate-3770_lsat-lr_base": {
131
+ "task": "ut-voluptate-3770_lsat-lr_base",
132
+ "group": "logikon-bench",
133
+ "dataset_path": "cot-leaderboard/cot-eval-traces",
134
+ "dataset_kwargs": {
135
+ "data_files": {
136
+ "test": "data/internlm/internlm2-chat-7b/ut-voluptate-3770-lsat-lr.parquet"
137
+ }
138
+ },
139
+ "test_split": "test",
140
+ "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
141
+ "doc_to_target": "{{answer}}",
142
+ "doc_to_choice": "{{options}}",
143
+ "description": "",
144
+ "target_delimiter": " ",
145
+ "fewshot_delimiter": "\n\n",
146
+ "num_fewshot": 0,
147
+ "metric_list": [
148
+ {
149
+ "metric": "acc",
150
+ "aggregation": "mean",
151
+ "higher_is_better": true
152
+ }
153
+ ],
154
+ "output_type": "multiple_choice",
155
+ "repeats": 1,
156
+ "should_decontaminate": false,
157
+ "metadata": {
158
+ "version": 0.0
159
+ }
160
+ },
161
+ "ut-voluptate-3770_lsat-rc_base": {
162
+ "task": "ut-voluptate-3770_lsat-rc_base",
163
+ "group": "logikon-bench",
164
+ "dataset_path": "cot-leaderboard/cot-eval-traces",
165
+ "dataset_kwargs": {
166
+ "data_files": {
167
+ "test": "data/internlm/internlm2-chat-7b/ut-voluptate-3770-lsat-rc.parquet"
168
+ }
169
+ },
170
+ "test_split": "test",
171
+ "doc_to_text": "def doc_to_text(doc) -> str:\n \"\"\"\n Answer the following question about the given passage.\n \n Passage: <passage>\n \n Question: <question>\n A. <choice1>\n B. <choice2>\n C. <choice3>\n D. <choice4>\n [E. <choice5>]\n \n Answer:\n \"\"\"\n k = len(doc[\"options\"])\n choices = [\"a\", \"b\", \"c\", \"d\", \"e\"][:k]\n prompt = \"Answer the following question about the given passage.\\n\\n\"\n prompt = \"Passage: \" + doc[\"passage\"] + \"\\n\\n\"\n prompt += \"Question: \" + doc[\"question\"] + \"\\n\"\n for choice, option in zip(choices, doc[\"options\"]):\n prompt += f\"{choice.upper()}. {option}\\n\"\n prompt += \"\\n\"\n prompt += \"Answer:\"\n return prompt\n",
172
+ "doc_to_target": "{{answer}}",
173
+ "doc_to_choice": "{{options}}",
174
+ "description": "",
175
+ "target_delimiter": " ",
176
+ "fewshot_delimiter": "\n\n",
177
+ "num_fewshot": 0,
178
+ "metric_list": [
179
+ {
180
+ "metric": "acc",
181
+ "aggregation": "mean",
182
+ "higher_is_better": true
183
+ }
184
+ ],
185
+ "output_type": "multiple_choice",
186
+ "repeats": 1,
187
+ "should_decontaminate": false,
188
+ "metadata": {
189
+ "version": 0.0
190
+ }
191
+ }
192
+ },
193
+ "versions": {
194
+ "ut-voluptate-3770_logiqa2_base": 0.0,
195
+ "ut-voluptate-3770_logiqa_base": 0.0,
196
+ "ut-voluptate-3770_lsat-ar_base": 0.0,
197
+ "ut-voluptate-3770_lsat-lr_base": 0.0,
198
+ "ut-voluptate-3770_lsat-rc_base": 0.0
199
+ },
200
+ "n-shot": {
201
+ "ut-voluptate-3770_logiqa2_base": 0,
202
+ "ut-voluptate-3770_logiqa_base": 0,
203
+ "ut-voluptate-3770_lsat-ar_base": 0,
204
+ "ut-voluptate-3770_lsat-lr_base": 0,
205
+ "ut-voluptate-3770_lsat-rc_base": 0
206
+ },
207
+ "config": {
208
+ "model": "vllm",
209
+ "model_args": "pretrained=internlm/internlm2-chat-7b,revision=main,dtype=bfloat16,tensor_parallel_size=4,gpu_memory_utilization=0.7,trust_remote_code=true,max_length=2048",
210
+ "batch_size": "auto",
211
+ "batch_sizes": [],
212
+ "device": null,
213
+ "use_cache": null,
214
+ "limit": null,
215
+ "bootstrap_iters": 100000,
216
+ "gen_kwargs": null
217
+ },
218
+ "git_hash": "f3c749c",
219
+ "date": 1715229826.9433393,
220
+ "pretty_env_info": "PyTorch version: 2.1.2+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 3.27.6\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-5.15.0-60-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 12.2.140\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA RTX A6000\nGPU 1: NVIDIA RTX A6000\nGPU 2: NVIDIA RTX A6000\nGPU 3: NVIDIA RTX A6000\n\nNvidia driver version: 525.105.17\ncuDNN version: Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.5\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.5\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): 128\nOn-line CPU(s) list: 0-127\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7502 32-Core Processor\nCPU family: 23\nModel: 49\nThread(s) per core: 2\nCore(s) per socket: 32\nSocket(s): 2\nStepping: 0\nFrequency boost: enabled\nCPU max MHz: 2500.0000\nCPU min MHz: 1500.0000\nBogoMIPS: 5000.15\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 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 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 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 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es\nVirtualization: AMD-V\nL1d cache: 2 MiB (64 instances)\nL1i cache: 2 MiB (64 instances)\nL2 cache: 32 MiB (64 instances)\nL3 cache: 256 MiB (16 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-31,64-95\nNUMA node1 CPU(s): 32-63,96-127\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 enabled with STIBP protection\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, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] mypy-extensions==1.0.0\n[pip3] numpy==1.22.2\n[pip3] pytorch-quantization==2.1.2\n[pip3] torch==2.1.2\n[pip3] torch-tensorrt==0.0.0\n[pip3] torchdata==0.7.0a0\n[pip3] torchtext==0.16.0a0\n[pip3] torchvision==0.16.0a0\n[pip3] triton==2.1.0+e621604\n[conda] Could not collect",
221
+ "transformers_version": "4.40.0",
222
+ "upper_git_hash": null
223
+ }