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Upload results for model mistralai/Mixtral-8x7B-Instruct-v0.1 (#248)

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- Upload results for model mistralai/Mixtral-8x7B-Instruct-v0.1 (0999b98af60b9330cbd2a5b82b823da3d82ce2ac)


Co-authored-by: Kyle Richardson <yakazimir@users.noreply.huggingface.co>

data/mistralai/Mixtral-8x7B-Instruct-v0.1/cot/24-04-09-09:24:28_idx10.json ADDED
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+ {
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+ "results": {
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+ "perspiciatis-iusto-6659_logiqa2_cot": {
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+ "acc,none": 0.5095419847328244,
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+ "acc_stderr,none": 0.012612547442024318,
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+ "alias": "perspiciatis-iusto-6659_logiqa2_cot"
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+ },
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+ "perspiciatis-iusto-6659_logiqa_cot": {
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+ "acc,none": 0.38338658146964855,
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+ "acc_stderr,none": 0.019448447161402607,
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+ "alias": "perspiciatis-iusto-6659_logiqa_cot"
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+ },
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+ "perspiciatis-iusto-6659_lsat-ar_cot": {
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+ "acc,none": 0.22608695652173913,
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+ "acc_stderr,none": 0.027641785707241327,
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+ "alias": "perspiciatis-iusto-6659_lsat-ar_cot"
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+ },
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+ "perspiciatis-iusto-6659_lsat-lr_cot": {
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+ "acc,none": 0.5137254901960784,
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+ "acc_stderr,none": 0.022153758586005032,
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+ "alias": "perspiciatis-iusto-6659_lsat-lr_cot"
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+ },
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+ "perspiciatis-iusto-6659_lsat-rc_cot": {
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+ "acc,none": 0.6133828996282528,
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+ "acc_stderr,none": 0.02974671172545301,
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+ "alias": "perspiciatis-iusto-6659_lsat-rc_cot"
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+ }
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+ },
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+ "configs": {
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+ "perspiciatis-iusto-6659_logiqa2_cot": {
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+ "task": "perspiciatis-iusto-6659_logiqa2_cot",
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+ "group": "logikon-bench",
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+ "dataset_path": "cot-leaderboard/cot-eval-traces",
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+ "dataset_kwargs": {
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+ "data_files": {
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+ "test": "perspiciatis-iusto-6659-logiqa2/test-00000-of-00001.parquet"
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+ }
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+ },
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+ "test_split": "test",
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+ "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\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 [Reasoning: <reasoning>]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
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+ "doc_to_target": "{{answer}}",
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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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+ "should_decontaminate": false,
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+ "metadata": {
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+ },
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+ "perspiciatis-iusto-6659_logiqa_cot": {
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+ "task": "perspiciatis-iusto-6659_logiqa_cot",
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+ "group": "logikon-bench",
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+ "dataset_path": "cot-leaderboard/cot-eval-traces",
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+ "dataset_kwargs": {
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+ "data_files": {
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+ "test": "perspiciatis-iusto-6659-logiqa/test-00000-of-00001.parquet"
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+ }
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+ },
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+ "test_split": "test",
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+ "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\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 [Reasoning: <reasoning>]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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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+ "target_delimiter": " ",
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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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+ },
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+ "perspiciatis-iusto-6659_lsat-ar_cot": {
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+ "task": "perspiciatis-iusto-6659_lsat-ar_cot",
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+ "group": "logikon-bench",
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+ "dataset_path": "cot-leaderboard/cot-eval-traces",
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+ "dataset_kwargs": {
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+ "data_files": {
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+ "test": "perspiciatis-iusto-6659-lsat-ar/test-00000-of-00001.parquet"
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+ }
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+ },
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+ "test_split": "test",
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+ "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\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 [Reasoning: <reasoning>]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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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+ "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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+ "perspiciatis-iusto-6659_lsat-lr_cot": {
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+ "task": "perspiciatis-iusto-6659_lsat-lr_cot",
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+ "group": "logikon-bench",
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+ "dataset_path": "cot-leaderboard/cot-eval-traces",
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+ "dataset_kwargs": {
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+ "data_files": {
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+ "test": "perspiciatis-iusto-6659-lsat-lr/test-00000-of-00001.parquet"
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+ }
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+ },
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+ "test_split": "test",
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+ "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\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 [Reasoning: <reasoning>]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\n\" \n prompt += \"Answer:\"\n return prompt\n",
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+ "doc_to_target": "{{answer}}",
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+ "target_delimiter": " ",
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+ "metadata": {
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+ }
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+ },
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+ "perspiciatis-iusto-6659_lsat-rc_cot": {
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+ "task": "perspiciatis-iusto-6659_lsat-rc_cot",
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+ "group": "logikon-bench",
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+ "dataset_path": "cot-leaderboard/cot-eval-traces",
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+ "dataset_kwargs": {
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+ "data_files": {
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+ "test": "perspiciatis-iusto-6659-lsat-rc/test-00000-of-00001.parquet"
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+ }
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+ },
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+ "test_split": "test",
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+ "doc_to_text": "def doc_to_text_cot(doc) -> str:\n \"\"\"\n Answer the following question about the given passage. [Base your answer on the reasoning below.]\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 [Reasoning: <reasoning>]\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. Base your answer on the reasoning below.\\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 += \"Reasoning: \" + doc[\"reasoning_trace\"] + \"\\n\\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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+ "target_delimiter": " ",
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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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+ "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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+ },
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+ "versions": {
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+ },
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+ },
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+ "config": {
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+ "model": "vllm",
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+ "model_args": "pretrained=mistralai/Mixtral-8x7B-Instruct-v0.1,revision=main,dtype=bfloat16,tensor_parallel_size=4,gpu_memory_utilization=0.8,trust_remote_code=true,max_length=2048",
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+ "batch_size": "auto",
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+ "git_hash": "741db1c"
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+ }