Upload results for model Locutusque/Hercules-4.0-Mistral-v0.2-7B
#178
by
ggbetz
- opened
data/Locutusque/Hercules-4.0-Mistral-v0.2-7B/base/24-04-08-10:47:24.json
ADDED
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| 1 |
+
{
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| 2 |
+
"results": {
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| 3 |
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"voluptatem-ad-8562_logiqa2_base": {
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| 4 |
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"acc,none": 0.31679389312977096,
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| 5 |
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"acc_stderr,none": 0.011737515474466387,
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| 6 |
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"alias": "voluptatem-ad-8562_logiqa2_base"
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| 7 |
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},
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"voluptatem-ad-8562_logiqa_base": {
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| 9 |
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"acc,none": 0.29233226837060705,
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| 10 |
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"acc_stderr,none": 0.018193366406024092,
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| 11 |
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"alias": "voluptatem-ad-8562_logiqa_base"
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| 12 |
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},
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| 13 |
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"voluptatem-ad-8562_lsat-ar_base": {
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| 14 |
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"acc,none": 0.20869565217391303,
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| 15 |
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"acc_stderr,none": 0.02685410826543968,
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| 16 |
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"alias": "voluptatem-ad-8562_lsat-ar_base"
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| 17 |
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},
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| 18 |
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"voluptatem-ad-8562_lsat-lr_base": {
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| 19 |
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"acc,none": 0.20784313725490197,
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| 20 |
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"acc_stderr,none": 0.017985170028791075,
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| 21 |
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"alias": "voluptatem-ad-8562_lsat-lr_base"
|
| 22 |
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},
|
| 23 |
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"voluptatem-ad-8562_lsat-rc_base": {
|
| 24 |
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"acc,none": 0.3048327137546468,
|
| 25 |
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"acc_stderr,none": 0.028119529675613444,
|
| 26 |
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"alias": "voluptatem-ad-8562_lsat-rc_base"
|
| 27 |
+
}
|
| 28 |
+
},
|
| 29 |
+
"configs": {
|
| 30 |
+
"voluptatem-ad-8562_logiqa2_base": {
|
| 31 |
+
"task": "voluptatem-ad-8562_logiqa2_base",
|
| 32 |
+
"group": "logikon-bench",
|
| 33 |
+
"dataset_path": "cot-leaderboard/cot-eval-traces",
|
| 34 |
+
"dataset_kwargs": {
|
| 35 |
+
"data_files": {
|
| 36 |
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"test": "voluptatem-ad-8562-logiqa2/test-00000-of-00001.parquet"
|
| 37 |
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}
|
| 38 |
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},
|
| 39 |
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"test_split": "test",
|
| 40 |
+
"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",
|
| 41 |
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"doc_to_target": "{{answer}}",
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| 42 |
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"doc_to_choice": "{{options}}",
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| 43 |
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"description": "",
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| 44 |
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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| 46 |
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"num_fewshot": 0,
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| 47 |
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"metric_list": [
|
| 48 |
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{
|
| 49 |
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"metric": "acc",
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| 50 |
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"aggregation": "mean",
|
| 51 |
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"higher_is_better": true
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| 52 |
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}
|
| 53 |
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],
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| 54 |
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"output_type": "multiple_choice",
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| 55 |
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"repeats": 1,
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| 56 |
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"should_decontaminate": false,
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| 57 |
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"metadata": {
|
| 58 |
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"version": 0.0
|
| 59 |
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}
|
| 60 |
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},
|
| 61 |
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"voluptatem-ad-8562_logiqa_base": {
|
| 62 |
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"task": "voluptatem-ad-8562_logiqa_base",
|
| 63 |
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"group": "logikon-bench",
|
| 64 |
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"dataset_path": "cot-leaderboard/cot-eval-traces",
|
| 65 |
+
"dataset_kwargs": {
|
| 66 |
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"data_files": {
|
| 67 |
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"test": "voluptatem-ad-8562-logiqa/test-00000-of-00001.parquet"
|
| 68 |
+
}
|
| 69 |
+
},
|
| 70 |
+
"test_split": "test",
|
| 71 |
+
"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",
|
| 72 |
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"doc_to_target": "{{answer}}",
|
| 73 |
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"doc_to_choice": "{{options}}",
|
| 74 |
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"description": "",
|
| 75 |
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"target_delimiter": " ",
|
| 76 |
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"fewshot_delimiter": "\n\n",
|
| 77 |
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"num_fewshot": 0,
|
| 78 |
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"metric_list": [
|
| 79 |
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{
|
| 80 |
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"metric": "acc",
|
| 81 |
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"aggregation": "mean",
|
| 82 |
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"higher_is_better": true
|
| 83 |
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}
|
| 84 |
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],
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| 85 |
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"output_type": "multiple_choice",
|
| 86 |
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"repeats": 1,
|
| 87 |
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"should_decontaminate": false,
|
| 88 |
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"metadata": {
|
| 89 |
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"version": 0.0
|
| 90 |
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}
|
| 91 |
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},
|
| 92 |
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"voluptatem-ad-8562_lsat-ar_base": {
|
| 93 |
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"task": "voluptatem-ad-8562_lsat-ar_base",
|
| 94 |
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"group": "logikon-bench",
|
| 95 |
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"dataset_path": "cot-leaderboard/cot-eval-traces",
|
| 96 |
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"dataset_kwargs": {
|
| 97 |
+
"data_files": {
|
| 98 |
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"test": "voluptatem-ad-8562-lsat-ar/test-00000-of-00001.parquet"
|
| 99 |
+
}
|
| 100 |
+
},
|
| 101 |
+
"test_split": "test",
|
| 102 |
+
"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",
|
| 103 |
+
"doc_to_target": "{{answer}}",
|
| 104 |
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"doc_to_choice": "{{options}}",
|
| 105 |
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"description": "",
|
| 106 |
+
"target_delimiter": " ",
|
| 107 |
+
"fewshot_delimiter": "\n\n",
|
| 108 |
+
"num_fewshot": 0,
|
| 109 |
+
"metric_list": [
|
| 110 |
+
{
|
| 111 |
+
"metric": "acc",
|
| 112 |
+
"aggregation": "mean",
|
| 113 |
+
"higher_is_better": true
|
| 114 |
+
}
|
| 115 |
+
],
|
| 116 |
+
"output_type": "multiple_choice",
|
| 117 |
+
"repeats": 1,
|
| 118 |
+
"should_decontaminate": false,
|
| 119 |
+
"metadata": {
|
| 120 |
+
"version": 0.0
|
| 121 |
+
}
|
| 122 |
+
},
|
| 123 |
+
"voluptatem-ad-8562_lsat-lr_base": {
|
| 124 |
+
"task": "voluptatem-ad-8562_lsat-lr_base",
|
| 125 |
+
"group": "logikon-bench",
|
| 126 |
+
"dataset_path": "cot-leaderboard/cot-eval-traces",
|
| 127 |
+
"dataset_kwargs": {
|
| 128 |
+
"data_files": {
|
| 129 |
+
"test": "voluptatem-ad-8562-lsat-lr/test-00000-of-00001.parquet"
|
| 130 |
+
}
|
| 131 |
+
},
|
| 132 |
+
"test_split": "test",
|
| 133 |
+
"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",
|
| 134 |
+
"doc_to_target": "{{answer}}",
|
| 135 |
+
"doc_to_choice": "{{options}}",
|
| 136 |
+
"description": "",
|
| 137 |
+
"target_delimiter": " ",
|
| 138 |
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"fewshot_delimiter": "\n\n",
|
| 139 |
+
"num_fewshot": 0,
|
| 140 |
+
"metric_list": [
|
| 141 |
+
{
|
| 142 |
+
"metric": "acc",
|
| 143 |
+
"aggregation": "mean",
|
| 144 |
+
"higher_is_better": true
|
| 145 |
+
}
|
| 146 |
+
],
|
| 147 |
+
"output_type": "multiple_choice",
|
| 148 |
+
"repeats": 1,
|
| 149 |
+
"should_decontaminate": false,
|
| 150 |
+
"metadata": {
|
| 151 |
+
"version": 0.0
|
| 152 |
+
}
|
| 153 |
+
},
|
| 154 |
+
"voluptatem-ad-8562_lsat-rc_base": {
|
| 155 |
+
"task": "voluptatem-ad-8562_lsat-rc_base",
|
| 156 |
+
"group": "logikon-bench",
|
| 157 |
+
"dataset_path": "cot-leaderboard/cot-eval-traces",
|
| 158 |
+
"dataset_kwargs": {
|
| 159 |
+
"data_files": {
|
| 160 |
+
"test": "voluptatem-ad-8562-lsat-rc/test-00000-of-00001.parquet"
|
| 161 |
+
}
|
| 162 |
+
},
|
| 163 |
+
"test_split": "test",
|
| 164 |
+
"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",
|
| 165 |
+
"doc_to_target": "{{answer}}",
|
| 166 |
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"doc_to_choice": "{{options}}",
|
| 167 |
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"description": "",
|
| 168 |
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"target_delimiter": " ",
|
| 169 |
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"fewshot_delimiter": "\n\n",
|
| 170 |
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"num_fewshot": 0,
|
| 171 |
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"metric_list": [
|
| 172 |
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{
|
| 173 |
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"metric": "acc",
|
| 174 |
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"aggregation": "mean",
|
| 175 |
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"higher_is_better": true
|
| 176 |
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}
|
| 177 |
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],
|
| 178 |
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"output_type": "multiple_choice",
|
| 179 |
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"repeats": 1,
|
| 180 |
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"should_decontaminate": false,
|
| 181 |
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"metadata": {
|
| 182 |
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"version": 0.0
|
| 183 |
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}
|
| 184 |
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}
|
| 185 |
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},
|
| 186 |
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"versions": {
|
| 187 |
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"voluptatem-ad-8562_logiqa2_base": 0.0,
|
| 188 |
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"voluptatem-ad-8562_logiqa_base": 0.0,
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| 189 |
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"voluptatem-ad-8562_lsat-ar_base": 0.0,
|
| 190 |
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"voluptatem-ad-8562_lsat-lr_base": 0.0,
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| 191 |
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"voluptatem-ad-8562_lsat-rc_base": 0.0
|
| 192 |
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},
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| 193 |
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"n-shot": {
|
| 194 |
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"voluptatem-ad-8562_logiqa2_base": 0,
|
| 195 |
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"voluptatem-ad-8562_logiqa_base": 0,
|
| 196 |
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"voluptatem-ad-8562_lsat-ar_base": 0,
|
| 197 |
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"voluptatem-ad-8562_lsat-lr_base": 0,
|
| 198 |
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"voluptatem-ad-8562_lsat-rc_base": 0
|
| 199 |
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},
|
| 200 |
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"config": {
|
| 201 |
+
"model": "vllm",
|
| 202 |
+
"model_args": "pretrained=Locutusque/Hercules-4.0-Mistral-v0.2-7B,revision=main,dtype=bfloat16,tensor_parallel_size=1,gpu_memory_utilization=0.8,trust_remote_code=true,max_length=2048",
|
| 203 |
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"batch_size": "auto",
|
| 204 |
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"batch_sizes": [],
|
| 205 |
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"device": null,
|
| 206 |
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"use_cache": null,
|
| 207 |
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"limit": null,
|
| 208 |
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"bootstrap_iters": 100000,
|
| 209 |
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"gen_kwargs": null
|
| 210 |
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},
|
| 211 |
+
"git_hash": "741db1c"
|
| 212 |
+
}
|