{"event": "preflight", "before": {"native": false, "qwen4b": true, "ollama": true}, "context": 32768, "output_limit": 256, "note": "Installed quantizations and draft settings differ; not an engine-only comparison."}
{"event": "startup", "runtime": "native", "ready_s": 8.02236750398879}
{"event": "prompts", "values": {"code": "<|im_start|>user\nImplement a Python function dedupe(items, key) preserving first occurrence order. Return only code with type hints, a short docstring, and three assertions covering empty input, duplicate IDs and stable order. Do not explain.<|im_end|>\n<|im_start|>assistant\n\n\n\n\n", "intel": "<|im_start|>user\nReturn JSON only with keys summary, facts, uncertainties, next_actions. Evidence: Acme reported revenue of 12 million versus 10 million last year. Operating expense rose from 7 to 9 million. Cash is 4 million. The CEO expects growth next year but supplied no forecast. Distinguish reported facts from predictions and calculate revenue growth.<|im_end|>\n<|im_start|>assistant\n\n\n\n\n", "plan_code": "<|im_start|>user\nContext note 0: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 1: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 2: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 3: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 4: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 5: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 6: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 7: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 8: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 9: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 10: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 11: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 12: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 13: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 14: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 15: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 16: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 17: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 18: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 19: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 20: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 21: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 22: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 23: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 24: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 25: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 26: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 27: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 28: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 29: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 30: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 31: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 32: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 33: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 34: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 35: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 36: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 37: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 38: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nContext note 39: requests must validate input, preserve order, avoid shared mutable state, and handle missing keys explicitly.\nImplement this approved plan in Python: accept records with id and value; reject missing id; merge duplicates keeping the last value but first-seen id order; return a new list without changing inputs. Return only the function and three assertions.<|im_end|>\n<|im_start|>assistant\n\n\n\n\n"}}
{"event": "sample", "runtime": "native", "case": "first_request", "repeat": 0, "output_limit": 32, "prompt_sha256": "db605caba9222a3b2ea796aaff934f46b1892ac8de322d56bf20c664e37aca05", "ttft_s": 2.0506415509880753, "elapsed_s": 5.182967074986664, "output_tokens": 32, "decode_tps": 10.216761363273552, "evaluated_prompt_tokens": 84, "prefill_tps": 40.99254667660248, "timing": {"cache_n": 0, "prompt_n": 84, "prompt_ms": 2049.153, "prompt_per_token_ms": 24.394678571428567, "prompt_per_second": 40.99254667660248, "predicted_n": 32, "predicted_ms": 3132.108, "predicted_per_token_ms": 101.03574193548387, "predicted_per_second": 9.897487570671252, "draft_n": 29, "draft_n_accepted": 21}, "output": "{\n \"summary\": \"Acme experienced 20% revenue growth year-over-year, rising from 10 million to 12 million,"}
{"event": "sample", "runtime": "native", "case": "code", "repeat": 0, "output_limit": 256, "prompt_sha256": "d41635ab24b1bed388eb23aae7a167210287987a6538089181fed776318b67ac", "ttft_s": 1.315801367003587, "elapsed_s": 18.385247575002722, "output_tokens": 205, "decode_tps": 12.009964169297142, "evaluated_prompt_tokens": 56, "prefill_tps": 47.518474928488935, "timing": {"cache_n": 0, "prompt_n": 56, "prompt_ms": 1178.489, "prompt_per_token_ms": 21.04444642857143, "prompt_per_second": 47.51847492848894, "predicted_n": 205, "predicted_ms": 17069.16, "predicted_per_token_ms": 83.67235294117647, "predicted_per_second": 11.9513789782274, "draft_n": 162, "draft_n_accepted": 150}, "output": "```python\nfrom typing import Any, Callable, Iterable, List\n\ndef dedupe(items: Iterable[Any], key: Callable[[Any], Any]) -> List[Any]:\n \"\"\"Deduplicate items preserving first occurrence order.\"\"\"\n seen: set = set()\n result: List[Any] = []\n for item in items:\n k = key(item)\n if k not in seen:\n seen.add(k)\n result.append(item)\n return result\n\nassert dedupe([], lambda x: x) == []\nassert dedupe([1, 2, 1, 3, 2], lambda x: x) == [1, 2, 3]\nassert dedupe([{\"id\": 1}, {\"id\": 2}, {\"id\": 1}, {\"id\": 3}], lambda x: x[\"id\"]) == [{\"id\": 1}, {\"id\": 2}, {\"id\": 3}]\n```"}
{"event": "sample", "runtime": "native", "case": "intel", "repeat": 0, "output_limit": 256, "prompt_sha256": "db605caba9222a3b2ea796aaff934f46b1892ac8de322d56bf20c664e37aca05", "ttft_s": 2.1660247270046966, "elapsed_s": 26.426927028995124, "output_tokens": 256, "decode_tps": 10.552052906014762, "evaluated_prompt_tokens": 84, "prefill_tps": 42.04806093364716, "timing": {"cache_n": 0, "prompt_n": 84, "prompt_ms": 1997.714, "prompt_per_token_ms": 23.782309523809523, "prompt_per_second": 42.04806093364716, "predicted_n": 256, "predicted_ms": 24260.682, "predicted_per_token_ms": 95.13992941176471, "predicted_per_second": 10.510833949350639, "draft_n": 228, "draft_n_accepted": 178}, "output": "{\n \"summary\": \"Acme experienced 20% revenue growth year-over-year, rising from 10 million to 12 million, while operating expenses increased by 28.6% from 7 million to 9 million. The company currently holds 4 million in cash. The CEO anticipates future growth but has not provided specific financial forecasts.\",\n \"facts\": [\n \"Current year revenue is 12 million.\",\n \"Previous year revenue was 10 million.\",\n \"Current year operating expense is 9 million.\",\n \"Previous year operating expense was 7 million.\",\n \"Current cash balance is 4 million.\",\n \"Revenue growth rate is 20%.\"\n ],\n \"uncertainties\": [\n \"The CEO's expectation of growth next year is a prediction without a supplied forecast, making the magnitude and likelihood of future growth uncertain.\"\n ],\n \"next_actions\": [\n \"Request a detailed financial forecast from the CEO to quantify expected growth.\",\n \"Analyze the impact of the higher operating expense growth rate (28.6%) compared to revenue growth (20%) on future margins.\"\n ]\n}"}
{"event": "sample", "runtime": "native", "case": "plan_code", "repeat": 0, "output_limit": 256, "prompt_sha256": "ace5e2e4dcdf19dd754c043e1a4f386609d52d51202b432fa36135a1340a1b63", "ttft_s": 14.277268013000139, "elapsed_s": 38.748283569002524, "output_tokens": 256, "decode_tps": 10.461439379636644, "evaluated_prompt_tokens": 1089, "prefill_tps": 77.07354373113239, "timing": {"cache_n": 0, "prompt_n": 1089, "prompt_ms": 14129.362, "prompt_per_token_ms": 12.97462075298439, "prompt_per_second": 77.07354373113238, "predicted_n": 256, "predicted_ms": 24470.82, "predicted_per_token_ms": 95.964, "predicted_per_second": 10.42057438205994, "draft_n": 228, "draft_n_accepted": 178}, "output": "```python\ndef merge_records(records):\n \"\"\"\n Accept records with id and value; reject missing id; merge duplicates keeping the last value but first-seen id order; return a new list without changing inputs.\n \"\"\"\n if not isinstance(records, list):\n raise TypeError(\"Input must be a list\")\n \n # Validate each record\n for i, record in enumerate(records):\n if not isinstance(record, dict):\n raise TypeError(f\"Record at index {i} must be a dict\")\n if 'id' not in record:\n raise ValueError(f\"Record at index {i} is missing 'id'\")\n \n # Use a dict to track first-seen order and last value\n # Key: id, Value: (first_seen_index, last_value)\n seen = {}\n order = [] # list of ids in first-seen order\n \n for record in records:\n rid = record['id']\n value = record.get('value') # value may be missing, that's okay per spec (only id is required)\n \n if rid not in seen:\n seen[rid] = value\n order.append("}
{"event": "sample", "runtime": "native", "case": "code", "repeat": 1, "output_limit": 256, "prompt_sha256": "d41635ab24b1bed388eb23aae7a167210287987a6538089181fed776318b67ac", "ttft_s": 1.425506953994045, "elapsed_s": 18.51088918300229, "output_tokens": 205, "decode_tps": 11.99883640552321, "evaluated_prompt_tokens": 56, "prefill_tps": 47.12876419964418, "timing": {"cache_n": 0, "prompt_n": 56, "prompt_ms": 1188.234, "prompt_per_token_ms": 21.218464285714283, "prompt_per_second": 47.12876419964418, "predicted_n": 205, "predicted_ms": 17084.99, "predicted_per_token_ms": 83.74995098039217, "predicted_per_second": 11.940305496227975, "draft_n": 162, "draft_n_accepted": 150}, "output": "```python\nfrom typing import Any, Callable, Iterable, List\n\ndef dedupe(items: Iterable[Any], key: Callable[[Any], Any]) -> List[Any]:\n \"\"\"Deduplicate items preserving first occurrence order.\"\"\"\n seen: set = set()\n result: List[Any] = []\n for item in items:\n k = key(item)\n if k not in seen:\n seen.add(k)\n result.append(item)\n return result\n\nassert dedupe([], lambda x: x) == []\nassert dedupe([1, 2, 1, 3, 2], lambda x: x) == [1, 2, 3]\nassert dedupe([{\"id\": 1}, {\"id\": 2}, {\"id\": 1}, {\"id\": 3}], lambda x: x[\"id\"]) == [{\"id\": 1}, {\"id\": 2}, {\"id\": 3}]\n```"}
{"event": "sample", "runtime": "native", "case": "intel", "repeat": 1, "output_limit": 256, "prompt_sha256": "db605caba9222a3b2ea796aaff934f46b1892ac8de322d56bf20c664e37aca05", "ttft_s": 1.9453777570015518, "elapsed_s": 26.216626572000678, "output_tokens": 256, "decode_tps": 10.547572270234733, "evaluated_prompt_tokens": 84, "prefill_tps": 43.218168918213216, "timing": {"cache_n": 0, "prompt_n": 84, "prompt_ms": 1943.627, "prompt_per_token_ms": 23.138416666666664, "prompt_per_second": 43.218168918213216, "predicted_n": 256, "predicted_ms": 24270.988, "predicted_per_token_ms": 95.18034509803923, "predicted_per_second": 10.50637081605413, "draft_n": 228, "draft_n_accepted": 178}, "output": "{\n \"summary\": \"Acme experienced 20% revenue growth year-over-year, rising from 10 million to 12 million, while operating expenses increased by 28.6% from 7 million to 9 million. The company currently holds 4 million in cash. The CEO anticipates future growth but has not provided specific financial forecasts.\",\n \"facts\": [\n \"Current year revenue is 12 million.\",\n \"Previous year revenue was 10 million.\",\n \"Current year operating expense is 9 million.\",\n \"Previous year operating expense was 7 million.\",\n \"Current cash balance is 4 million.\",\n \"Revenue growth rate is 20%.\"\n ],\n \"uncertainties\": [\n \"The CEO's expectation of growth next year is a prediction without a supplied forecast, making the magnitude and likelihood of future growth uncertain.\"\n ],\n \"next_actions\": [\n \"Request a detailed financial forecast from the CEO to quantify expected growth.\",\n \"Analyze the impact of the higher operating expense growth rate (28.6%) compared to revenue growth (20%) on future margins.\"\n ]\n}"}
{"event": "sample", "runtime": "native", "case": "plan_code", "repeat": 1, "output_limit": 256, "prompt_sha256": "ace5e2e4dcdf19dd754c043e1a4f386609d52d51202b432fa36135a1340a1b63", "ttft_s": 14.165283615002409, "elapsed_s": 38.32779058199958, "output_tokens": 256, "decode_tps": 10.595048553051601, "evaluated_prompt_tokens": 1089, "prefill_tps": 77.09821299935341, "timing": {"cache_n": 0, "prompt_n": 1089, "prompt_ms": 14124.841, "prompt_per_token_ms": 12.970469237832875, "prompt_per_second": 77.09821299935341, "predicted_n": 256, "predicted_ms": 24162.23, "predicted_per_token_ms": 94.7538431372549, "predicted_per_second": 10.553661644641243, "draft_n": 225, "draft_n_accepted": 179}, "output": "```python\ndef merge_records(records):\n \"\"\"\n Accept records with id and value; reject missing id; merge duplicates keeping the last value but first-seen id order; return a new list without changing inputs.\n \"\"\"\n if not isinstance(records, list):\n raise TypeError(\"Input must be a list\")\n \n # Validate each record\n for i, record in enumerate(records):\n if not isinstance(record, dict):\n raise TypeError(f\"Record at index {i} must be a dict\")\n if 'id' not in record:\n raise ValueError(f\"Record at index {i} is missing 'id'\")\n \n # Use a dict to track first-seen order and last value\n # Key: id, Value: (first_seen_index, last_value)\n seen = {}\n order = [] # list of ids in first-seen order\n \n for record in records:\n rid = record['id']\n value = record.get('value') # value may be missing, that's okay per spec (only id is required)\n \n if rid not in seen:\n seen[rid] = value\n order.append("}
{"event": "startup", "runtime": "ollama_daemon", "ready_s": 2.0091152370005148}
{"event": "sample", "runtime": "ollama", "case": "first_request", "repeat": 0, "output_limit": 32, "prompt_sha256": "db605caba9222a3b2ea796aaff934f46b1892ac8de322d56bf20c664e37aca05", "ttft_s": 9.746642072001123, "elapsed_s": 12.748977652998292, "output_tokens": 32, "decode_tps": 10.657167577965508, "evaluated_prompt_tokens": 84, "prefill_tps": 40.94429236564423, "timing": {"total_duration": 12748400733, "load_duration": 7692297060, "prompt_eval_count": 84, "prompt_eval_cached_count": 0, "prompt_eval_duration": 2051568000, "eval_count": 32, "eval_duration": 3002674000}, "output": "{\n \"summary\": \"Acme reported a 20% year-over-year revenue increase to 12 million, while operating expenses rose by "}
{"event": "placement", "runtime": "ollama", "value": {"models": [{"name": "qwen3.8:27b", "model": "qwen3.8:27b", "size": 17536049478, "digest": "aaee06c39dcf2437cde036998d960e1fc1494b8191be7cc9657d01e509097813", "details": {"parent_model": "", "format": "gguf", "family": "qwen35", "families": ["qwen35"], "parameter_size": "27.3B", "quantization_level": "Q4_K_M"}, "expires_at": "2026-09-26T15:50:30.242888681+05:45", "size_vram": 17536049478, "context_length": 32768}]}}
{"event": "sample", "runtime": "ollama", "case": "code", "repeat": 0, "output_limit": 256, "prompt_sha256": "d41635ab24b1bed388eb23aae7a167210287987a6538089181fed776318b67ac", "ttft_s": 1.3094120750029106, "elapsed_s": 18.28552710999793, "output_tokens": 210, "decode_tps": 12.370379773604379, "evaluated_prompt_tokens": 56, "prefill_tps": 47.93071956848658, "timing": {"total_duration": 18284963664, "load_duration": 1088600, "prompt_eval_count": 56, "prompt_eval_cached_count": 0, "prompt_eval_duration": 1168353000, "eval_count": 210, "eval_duration": 16976035000}, "output": "```python\nfrom typing import Any, Callable, Iterable, List\n\n\ndef dedupe(items: Iterable[Any], key: Callable[[Any], Any]) -> List[Any]:\n \"\"\"Deduplicate items preserving first occurrence order based on key.\"\"\"\n seen: set[Any] = set()\n result: List[Any] = []\n for item in items:\n k = key(item)\n if k not in seen:\n seen.add(k)\n result.append(item)\n return result\n\n\nassert dedupe([], lambda x: x) == []\nassert dedupe([{\"id\": 1}, {\"id\": 2}, {\"id\": 1}, {\"id\": 3}], lambda x: x[\"id\"]) == [{\"id\": 1}, {\"id\": 2}, {\"id\": 3}]\nassert dedupe([\"a\", \"b\", \"a\", \"c\", \"b\"], lambda x: x) == [\"a\", \"b\", \"c\"]\n```"}
{"event": "sample", "runtime": "ollama", "case": "intel", "repeat": 0, "output_limit": 256, "prompt_sha256": "db605caba9222a3b2ea796aaff934f46b1892ac8de322d56bf20c664e37aca05", "ttft_s": 0.5513509920128854, "elapsed_s": 24.427035497006727, "output_tokens": 256, "decode_tps": 10.722194230035463, "evaluated_prompt_tokens": 84, "prefill_tps": 222.250444500889, "timing": {"total_duration": 24426247856, "load_duration": 1116200, "prompt_eval_count": 84, "prompt_eval_cached_count": 80, "prompt_eval_duration": 377952000, "eval_count": 256, "eval_duration": 23875710000}, "output": "{\n \"summary\": \"Acme reported a 20% year-over-year revenue increase to 12 million, while operating expenses rose by 28.6% to 9 million. The company holds 4 million in cash. The CEO expressed expectations for future growth but provided no specific financial forecasts.\",\n \"facts\": [\n \"Current year revenue is 12 million.\",\n \"Previous year revenue was 10 million.\",\n \"Current year operating expense is 9 million.\",\n \"Previous year operating expense was 7 million.\",\n \"Current cash balance is 4 million.\",\n \"Revenue growth rate is 20%.\"\n ],\n \"uncertainties\": [\n \"The CEO's expectation of growth next year is a prediction, not a confirmed fact.\",\n \"No specific financial forecast or target for next year was provided.\"\n ],\n \"next_actions\": [\n \"Request a detailed financial forecast from the CEO to quantify expected growth.\",\n \"Analyze the impact of the higher operating expense ratio on future profitability.\",\n \"Monitor cash flow to ensure the 4 million cash reserve supports the increased operational costs.\"\n ]\n}"}
{"event": "sample", "runtime": "ollama", "case": "plan_code", "repeat": 0, "output_limit": 256, "prompt_sha256": "ace5e2e4dcdf19dd754c043e1a4f386609d52d51202b432fa36135a1340a1b63", "ttft_s": 15.760129541988135, "elapsed_s": 38.77866305099451, "output_tokens": 256, "decode_tps": 11.12155687894359, "evaluated_prompt_tokens": 1089, "prefill_tps": 69.7572301062981, "timing": {"total_duration": 38777919382, "load_duration": 1113355, "prompt_eval_count": 1089, "prompt_eval_cached_count": 0, "prompt_eval_duration": 15611285000, "eval_count": 256, "eval_duration": 23018360000}, "output": "```python\ndef merge_records(records):\n \"\"\"\n Accept records with id and value; reject missing id;\n merge duplicates keeping the last value but first-seen id order;\n return a new list without changing inputs.\n \"\"\"\n if not isinstance(records, list):\n raise TypeError(\"Input must be a list\")\n \n # Validate each record\n for i, record in enumerate(records):\n if not isinstance(record, dict):\n raise TypeError(f\"Record at index {i} must be a dict\")\n if 'id' not in record:\n raise ValueError(f\"Record at index {i} is missing 'id'\")\n if 'value' not in record:\n raise ValueError(f\"Record at index {i} is missing 'value'\")\n \n # Use a dict to track first-seen order and last value\n # OrderedDict or regular dict (Python 3.7+ preserves insertion order)\n merged = {}\n for record in records:\n rid = record['id']\n # If id already exists, update value (keep last value)\n # If not, insert (preserves first-seen order)\n"}
{"event": "sample", "runtime": "ollama", "case": "code", "repeat": 1, "output_limit": 256, "prompt_sha256": "d41635ab24b1bed388eb23aae7a167210287987a6538089181fed776318b67ac", "ttft_s": 1.4467116690066177, "elapsed_s": 18.43530440100585, "output_tokens": 210, "decode_tps": 12.361269764484923, "evaluated_prompt_tokens": 56, "prefill_tps": 46.45629706810991, "timing": {"total_duration": 18434335665, "load_duration": 1119757, "prompt_eval_count": 56, "prompt_eval_cached_count": 0, "prompt_eval_duration": 1205434000, "eval_count": 210, "eval_duration": 16988545999}, "output": "```python\nfrom typing import Any, Callable, Iterable, List\n\n\ndef dedupe(items: Iterable[Any], key: Callable[[Any], Any]) -> List[Any]:\n \"\"\"Deduplicate items preserving first occurrence order based on key.\"\"\"\n seen: set[Any] = set()\n result: List[Any] = []\n for item in items:\n k = key(item)\n if k not in seen:\n seen.add(k)\n result.append(item)\n return result\n\n\nassert dedupe([], lambda x: x) == []\nassert dedupe([{\"id\": 1}, {\"id\": 2}, {\"id\": 1}, {\"id\": 3}], lambda x: x[\"id\"]) == [{\"id\": 1}, {\"id\": 2}, {\"id\": 3}]\nassert dedupe([\"a\", \"b\", \"a\", \"c\", \"b\"], lambda x: x) == [\"a\", \"b\", \"c\"]\n```"}
{"event": "sample", "runtime": "ollama", "case": "intel", "repeat": 1, "output_limit": 256, "prompt_sha256": "db605caba9222a3b2ea796aaff934f46b1892ac8de322d56bf20c664e37aca05", "ttft_s": 1.9917228579870425, "elapsed_s": 25.911566258000676, "output_tokens": 254, "decode_tps": 10.618799237887943, "evaluated_prompt_tokens": 84, "prefill_tps": 42.25158707523951, "timing": {"total_duration": 25910915572, "load_duration": 1474084, "prompt_eval_count": 84, "prompt_eval_cached_count": 0, "prompt_eval_duration": 1988091000, "eval_count": 254, "eval_duration": 23919842000}, "output": "{\n \"summary\": \"Acme reported a 20% year-over-year revenue increase to 12 million, while operating expenses rose by 28.6% to 9 million. The company holds 4 million in cash. The CEO expressed expectations for future growth but provided no specific financial forecasts.\",\n \"facts\": [\n \"Current year revenue is 12 million.\",\n \"Previous year revenue was 10 million.\",\n \"Current year operating expense is 9 million.\",\n \"Previous year operating expense was 7 million.\",\n \"Current cash balance is 4 million.\",\n \"Revenue growth rate is 20%.\"\n ],\n \"uncertainties\": [\n \"The CEO's expectation of growth next year is a prediction without supporting data.\",\n \"No specific financial forecast was supplied for the next year.\"\n ],\n \"next_actions\": [\n \"Request a detailed financial forecast from the CEO to quantify expected growth.\",\n \"Analyze the impact of the higher operating expense ratio on future profitability.\",\n \"Monitor cash flow to ensure the 4 million cash reserve supports the increased operational costs.\"\n ]\n}"}
{"event": "sample", "runtime": "ollama", "case": "plan_code", "repeat": 1, "output_limit": 256, "prompt_sha256": "ace5e2e4dcdf19dd754c043e1a4f386609d52d51202b432fa36135a1340a1b63", "ttft_s": 0.5746844719978981, "elapsed_s": 24.28540248700301, "output_tokens": 256, "decode_tps": 10.796867440743016, "evaluated_prompt_tokens": 1089, "prefill_tps": 2778.8977294185493, "timing": {"total_duration": 24284679316, "load_duration": 1031243, "prompt_eval_count": 1089, "prompt_eval_cached_count": 1085, "prompt_eval_duration": 391882000, "eval_count": 256, "eval_duration": 23710581000}, "output": "```python\ndef merge_records(records):\n \"\"\"\n Accept records with id and value; reject missing id; merge duplicates keeping the last value but first-seen id order; return a new list without changing inputs.\n \"\"\"\n if not isinstance(records, list):\n raise TypeError(\"Input must be a list\")\n \n # Validate each record\n for i, record in enumerate(records):\n if not isinstance(record, dict):\n raise TypeError(f\"Record at index {i} must be a dict\")\n if 'id' not in record:\n raise ValueError(f\"Record at index {i} is missing 'id'\")\n if 'value' not in record:\n raise ValueError(f\"Record at index {i} is missing 'value'\")\n \n # Use a dict to track first-seen order and last value\n # We need to preserve first-seen id order, and for duplicates keep the last value\n # Use an ordered approach: iterate through records, track seen ids in order, and update values\n \n # First, determine the order of first-seen ids\n seen_ids = []\n id_to_index = {}\n for record in records"}
{"event": "restored", "native": false, "qwen4b": true, "ollama": true}