Upload proofs/88342.json with huggingface_hub

Browse files

Files changed (1) hide show

proofs/88342.json +1 -0

proofs/88342.json ADDED Viewed

	@@ -0,0 +1 @@

+ {"schema": 2, "epoch": 88342, "nonce": "77b78cc8b72790cc", "hotkey": "5FRE1kqMJz8ddModcXUsCquuWhB8QHnjYib2iK2VfiFetkC5", "source_hash": "1b6c06d6eb16e5e553195102d473d556ad2db09e4f698a599735242714efb202", "weights_hash": "f9ba14a964ea30142e764f44ef6523a81ddd943bd869d84b485d60d726d39b0e", "model_id": "injected", "total_cost_usd": 0.009006599999999998, "n_calls": 6, "call_log_hash": "235f5c41537cd86c529ddc1db324cbbf8e2cbf474e1028577b65ee70156c57e0", "measurement": "1449fadb4821cadef93f7eecc8c3b040e2cd244e01a2607ea531e5f7055c38d8", "confined": true, "latency_s": 23.185, "tokens_in": 1586, "tokens_out": 2478, "results": [{"benchmark": "mmlu", "task_id": "mmlu-12556", "answer": "A", "cost_usd": 0.001017, "chosen_rung": -1, "rungs_used": [], "distribution": []}, {"benchmark": "mmlu", "task_id": "mmlu-12315", "answer": "D", "cost_usd": 0.0008235, "chosen_rung": -1, "rungs_used": [], "distribution": []}, {"benchmark": "math", "task_id": "gsm8k-809", "answer": "1. Dale initially ordered 100 candies, but then ordered 100 more, giving him a total of 100 + 100 = 200 candies.\n2. He has already sold 150 candies.\n3. To find out how many candies he still needs to sell, subtract the number sold from the total number ordered: 200 - 150 = 50.\n\n50", "cost_usd": 0.0028874999999999994, "chosen_rung": -1, "rungs_used": [], "distribution": []}, {"benchmark": "math", "task_id": "gsm8k-578", "answer": "To find the final value of the number train, we can follow the operations step by step using the starting value of 20:\n\n1. **Find half the starting value:** \n $$20 \\div 2 = 10$$\n\n2. **Add this to the starting value:** \n $$20 + 10 = 30$$\n\n3. **Divide the result by 5:** \n $$30 \\div 5 = 6$$\n\n4. **Calculate the starting value minus 12:** \n $$20 - 12 = 8$$\n\n5. **Multiply the resulting value from step 3 by the result from step 4:** \n $$6 \\times 8 = 48$$\n\n48", "cost_usd": 0.0032639999999999995, "chosen_rung": -1, "rungs_used": [], "distribution": []}, {"benchmark": "code", "task_id": "lcb-abc399_b", "answer": "import sys\n\ndef main():\n data = list(map(int, sys.stdin.buffer.read().split()))\n n = data[0]\n scores = data[1:]\n ranks = [0] * n\n current_rank = 1\n\n for score in sorted(set(scores), reverse=True):\n indices = [i for i, value in enumerate(scores) if value == score]\n for i in indices:\n ranks[i] = current_rank\n current_rank += len(indices)\n\n sys.stdout.write(\"\\n\".join(map(str, ranks)))\n\nif __name__ == \"__main__\":\n main()\n", "cost_usd": 0.00014699999999999956, "chosen_rung": -1, "rungs_used": [], "distribution": []}, {"benchmark": "code", "task_id": "lcb-arc195_a", "answer": "import sys\nfrom bisect import bisect_left, bisect_right\nfrom collections import defaultdict\n\ndef main():\n input = sys.stdin.readline\n N, M = map(int, input().split())\n A = list(map(int, input().split()))\n B = list(map(int, input().split()))\n\n positions = defaultdict(list)\n for i, value in enumerate(A, 1):\n positions[value].append(i)\n\n earliest = [0] * (M + 1)\n prev = 0\n for i, value in enumerate(B, 1):\n pos_list = positions.get(value, [])\n j = bisect_left(pos_list, prev + 1)\n if j == len(pos_list):\n print(\"No\")\n return\n earliest[i] = pos_list[j]\n prev = earliest[i]\n\n latest = [N + 1] * (M + 2)\n next_pos = N + 1\n for i in range(M, 0, -1):\n pos_list = positions[B[i - 1]]\n j = bisect_left(pos_list, next_pos) - 1\n latest[i] = pos_list[j]\n next_pos = latest[i]\n\n for i, value in enumerate(B, 1):\n pos_list = positions[value]\n j = bisect_right(pos_list, earliest[i])\n if j < len(pos_list) and pos_list[j] < latest[i + 1]:\n print(\"Yes\")\n return\n\n print(\"No\")\n\nif __name__ == \"__main__\":\n main()\n", "cost_usd": 0.0008675999999999996, "chosen_rung": -1, "rungs_used": [], "distribution": []}], "quote": {"measurement": "1449fadb4821cadef93f7eecc8c3b040e2cd244e01a2607ea531e5f7055c38d8", "report_data": "7ad5bcf68fd1c25e00f602a1ba9422aca88629ca673382aea891a403dd57cea8", "platform_sig": "tdx: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"}}