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| "date": "2026-08-18", |
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| "alias": "gsm8k", |
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| "alias": "minerva_math500", |
| "sample_len": 500, |
| "exact_match,none": 0.394, |
| "exact_match_stderr,none": 0.021874299301689257, |
| "math_verify,none": 0.434, |
| "math_verify_stderr,none": 0.022187215803029008 |
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| "bbh_cot_fewshot": { |
| "alias": "bbh_cot_fewshot", |
| "name": "bbh_cot_fewshot", |
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| "alias": "mmlu_pro", |
| "name": "mmlu_pro", |
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| "alias": "ifeval", |
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| "exact_match,strict-match": 0.004464285714285714, |
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| "metadata": { |
| "version": 3.0, |
| "model": "quartz_r1_genesis_clean", |
| "base_url": "http://127.0.0.1:8080/v1/chat/completions", |
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| "dataset_path": "HuggingFaceH4/MATH-500", |
| "dataset_name": "default", |
| "test_split": "test", |
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| "doc_to_text": "def doc_to_text(doc: dict) -> str:\n return \"Problem:\" + \"\\n\" + doc[\"problem\"] + \"\\n\\n\" + \"Solution:\"\n", |
| "doc_to_target": "{{answer if few_shot is undefined else solution}}", |
| "unsafe_code": false, |
| "process_results": "def process_results(doc: dict, results: list[str]) -> dict[str, int]:\n candidates = results[0]\n\n unnormalized_answer = get_unnormalized_answer(candidates)\n answer = normalize_final_answer(unnormalized_answer)\n\n if is_equiv(answer, doc[\"answer\"]):\n retval = 1\n else:\n retval = 0\n\n # math_verify\n _mvres = verify(\n gold=parse(doc[\"solution\"]),\n target=parse(candidates),\n )\n mathval = 1 if _mvres else 0\n\n res = {\n \"exact_match\": retval,\n \"math_verify\": mathval,\n }\n return res\n", |
| "description": "", |
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| "dataset_path": "google/IFEval", |
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| { |
| "metric": "inst_level_strict_acc", |
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| "higher_is_better": true |
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| { |
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| "higher_is_better": true |
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| { |
| "metric": "inst_level_loose_acc", |
| "aggregation": "def agg_inst_level_acc(items):\n flat_items = [item for sublist in items for item in sublist]\n inst_level_acc = sum(flat_items) / len(flat_items)\n return inst_level_acc\n", |
| "higher_is_better": true |
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| "max_gen_toks": 1280 |
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| "repeats": 1, |
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| "metadata": { |
| "version": 4.0, |
| "model": "quartz_r1_genesis_clean", |
| "base_url": "http://127.0.0.1:8080/v1/chat/completions", |
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| "process_docs": "def process_docs(dataset):\n def _process(doc):\n return {\n \"id\": doc[\"query_id\"],\n \"passage\": doc[\"passage\"],\n \"question\": doc[\"question\"],\n \"answers\": get_answers(doc),\n }\n\n return dataset.map(_process)\n", |
| "doc_to_text": "{{passage}} {{question}}", |
| "doc_to_target": "{{ answer|join(',')}}", |
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| "process_results": "def process_results(doc, results):\n preds, golds = results, doc[\"answers\"]\n max_em = 0\n max_f1 = 0\n for gold_answer in golds:\n exact_match, f1_score = get_metrics(preds, gold_answer)\n if gold_answer[0].strip():\n max_em = max(max_em, exact_match)\n max_f1 = max(max_f1, f1_score)\n return {\"em\": max_em, \"f1\": max_f1}\n", |
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| "." |
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| "doc_to_decontamination_query": "{{passage}} {{question}}", |
| "metadata": { |
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| "gpqa_diamond_cot_zeroshot": { |
| "task": "gpqa_diamond_cot_zeroshot", |
| "dataset_path": "Idavidrein/gpqa", |
| "dataset_name": "gpqa_diamond", |
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| "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n choices = [\n preprocess(doc[\"Incorrect Answer 1\"]),\n preprocess(doc[\"Incorrect Answer 2\"]),\n preprocess(doc[\"Incorrect Answer 3\"]),\n preprocess(doc[\"Correct Answer\"]),\n ]\n\n random.shuffle(choices)\n correct_answer_index = choices.index(preprocess(doc[\"Correct Answer\"]))\n\n out_doc = {\n \"choice1\": choices[0],\n \"choice2\": choices[1],\n \"choice3\": choices[2],\n \"choice4\": choices[3],\n \"choices\": [choices[0], choices[1], choices[2], choices[3]],\n \"answer\": f\"({chr(65 + correct_answer_index)})\",\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", |
| "doc_to_text": "What is the correct answer to this question:{{Question}}\nChoices:\n(A) {{choice1}}\n(B) {{choice2}}\n(C) {{choice3}}\n(D) {{choice4}}\nLet's think step by step: ", |
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| "metadata": { |
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| "should_decontaminate": false, |
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| "base_url": "http://127.0.0.1:8080/v1/chat/completions", |
| "num_concurrent": 2, |
| "max_retries": 3, |
| "config_source": "/Users/vaultek/Documents/ai-fine-tuning/venv/lib/python3.11/site-packages/lm_eval/tasks/gpqa/cot_zeroshot/gpqa_main_cot_zeroshot.yaml" |
| } |
| }, |
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| "winogrande": {}, |
| "truthfulqa_mc2": {}, |
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| "mbpp": {} |
| } |
| } |
