Upload results for model nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
#959
by
ggbetz
- opened
data/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF/orig/results_24-10-17-04:44:21/nvidia__Llama-3.1-Nemotron-70B-Instruct-HF/results_2024-10-17T05-01-11.014110.json
ADDED
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1 |
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{
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"results": {
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"logiqa2_base": {
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"alias": "logiqa2_base",
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"logiqa_base": {
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"alias": "logiqa_base",
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"acc,none": 0.3961661341853035,
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"lsat-ar_base": {
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"alias": "lsat-ar_base",
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"acc_stderr,none": 0.029470189815005897
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"lsat-lr_base": {
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"alias": "lsat-lr_base",
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"acc_stderr,none": 0.02195236282810136
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},
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"lsat-rc_base": {
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"alias": "lsat-rc_base",
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"acc,none": 0.6245353159851301,
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"acc_stderr,none": 0.02957982843544668
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}
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},
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"group_subtasks": {
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"logiqa2_base": [],
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"logiqa_base": [],
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"lsat-ar_base": [],
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"lsat-lr_base": [],
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"lsat-rc_base": []
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},
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"configs": {
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"logiqa2_base": {
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"task": "logiqa2_base",
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"tag": "logikon-bench",
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"group": "logikon-bench",
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"dataset_path": "logikon/logikon-bench",
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"dataset_name": "logiqa2",
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"test_split": "test",
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"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",
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"doc_to_target": "{{answer}}",
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"doc_to_choice": "{{options}}",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"metric_list": [
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{
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"metric": "acc",
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"aggregation": "mean",
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"higher_is_better": true
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}
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],
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"output_type": "multiple_choice",
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"repeats": 1,
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"should_decontaminate": false,
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"metadata": {
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"task": "logiqa_base",
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"tag": "logikon-bench",
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"group": "logikon-bench",
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"dataset_path": "logikon/logikon-bench",
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"dataset_name": "logiqa",
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"test_split": "test",
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"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",
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"doc_to_target": "{{answer}}",
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"doc_to_choice": "{{options}}",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"metric_list": [
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{
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"metric": "acc",
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"aggregation": "mean",
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"higher_is_better": true
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}
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],
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"output_type": "multiple_choice",
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"repeats": 1,
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"should_decontaminate": false,
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"metadata": {
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"version": 0.0
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}
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},
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"lsat-ar_base": {
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"task": "lsat-ar_base",
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"tag": "logikon-bench",
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"group": "logikon-bench",
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"dataset_path": "logikon/logikon-bench",
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"dataset_name": "lsat-ar",
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"test_split": "test",
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"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",
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"doc_to_target": "{{answer}}",
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"doc_to_choice": "{{options}}",
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"description": "",
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"metric_list": [
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{
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"metric": "acc",
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"aggregation": "mean",
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"higher_is_better": true
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}
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],
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"output_type": "multiple_choice",
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"repeats": 1,
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"should_decontaminate": false,
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"metadata": {
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"version": 0.0
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}
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},
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"lsat-lr_base": {
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"task": "lsat-lr_base",
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"tag": "logikon-bench",
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"group": "logikon-bench",
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"dataset_path": "logikon/logikon-bench",
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"dataset_name": "lsat-lr",
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"test_split": "test",
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"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",
|
129 |
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"doc_to_target": "{{answer}}",
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130 |
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"doc_to_choice": "{{options}}",
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"description": "",
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132 |
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"target_delimiter": " ",
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"fewshot_delimiter": "\n\n",
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"metric_list": [
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{
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"metric": "acc",
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"aggregation": "mean",
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139 |
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"higher_is_better": true
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140 |
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}
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],
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"output_type": "multiple_choice",
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"repeats": 1,
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"should_decontaminate": false,
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145 |
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"metadata": {
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"version": 0.0
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147 |
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}
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},
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"lsat-rc_base": {
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"task": "lsat-rc_base",
|
151 |
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"tag": "logikon-bench",
|
152 |
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"group": "logikon-bench",
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153 |
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"dataset_path": "logikon/logikon-bench",
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154 |
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"dataset_name": "lsat-rc",
|
155 |
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"test_split": "test",
|
156 |
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"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",
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157 |
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"doc_to_target": "{{answer}}",
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"doc_to_choice": "{{options}}",
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"description": "",
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"target_delimiter": " ",
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"metric_list": [
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{
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"metric": "acc",
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"higher_is_better": true
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}
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],
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"output_type": "multiple_choice",
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"metadata": {
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}
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}
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},
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"versions": {
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},
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},
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},
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}
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},
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"config": {
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232 |
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"model": "local-completions",
|
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"model_args": "base_url=http://localhost:8080/v1/completions,num_concurrent=1,max_retries=3,tokenized_requests=False,model=nvidia/Llama-3.1-Nemotron-70B-Instruct-HF,trust_remote_code=True",
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"batch_size": "1",
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"batch_sizes": [],
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"device": null,
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"limit": null,
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"bootstrap_iters": 100000,
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"gen_kwargs": null,
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"random_seed": 0,
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"numpy_seed": 1234,
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"torch_seed": 1234,
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"fewshot_seed": 1234
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},
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"git_hash": "0a897fa",
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"date": 1729133067.4628808,
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