{
    "name": "qwen3-max@gsm8k",
    "dataset_name": "gsm8k",
    "dataset_pretty_name": "GSM8K",
    "dataset_description": "\n## Overview\n\nGSM8K (Grade School Math 8K) is a high-quality dataset of 8.5K linguistically diverse grade school math word problems created by human problem writers. The dataset is specifically designed to evaluate and improve the multi-step mathematical reasoning capabilities of language models.\n\n## Task Description\n\n- **Task Type**: Mathematical Word Problem Solving\n- **Input**: Natural language math word problem\n- **Output**: Numerical answer derived through step-by-step reasoning\n- **Difficulty**: Grade school level (2-8 reasoning steps required)\n\n## Key Features\n\n- Problems require basic arithmetic operations (addition, subtraction, multiplication, division)\n- Solutions involve 2 to 8 sequential reasoning steps\n- High linguistic diversity in problem formulations\n- Human-written problems ensuring natural language quality\n- Clear numerical answers for objective evaluation\n\n## Evaluation Notes\n\n- Default configuration uses **4-shot** examples with Chain-of-Thought (CoT) prompting\n- Answers should be formatted within `\\boxed{}` for proper extraction\n- The metric extracts numerical values for accuracy comparison\n- Supports both zero-shot and few-shot evaluation modes\n",
    "model_name": "qwen3-max",
    "score": 1.0,
    "metrics": [
        {
            "name": "mean_acc",
            "num": 10,
            "score": 1.0,
            "macro_score": 1.0,
            "categories": [
                {
                    "name": [
                        "default"
                    ],
                    "num": 10,
                    "score": 1.0,
                    "macro_score": 1.0,
                    "subsets": [
                        {
                            "name": "main",
                            "score": 1.0,
                            "num": 10
                        }
                    ]
                }
            ]
        }
    ],
    "analysis": "N/A",
    "perf_metrics": {
        "summary": {
            "n_samples": 10,
            "latency": {
                "mean": 7.26995,
                "std": 3.408364,
                "min": 3.010226,
                "25%": 4.80692,
                "50%": 6.124611,
                "75%": 10.165027,
                "90%": 11.61564,
                "99%": 12.700315,
                "max": 12.820834
            },
            "throughput": {
                "avg_output_tps": 29.86,
                "avg_req_ps": 0.1376
            },
            "usage": {
                "input_tokens": {
                    "mean": 662.6,
                    "std": 27.334146,
                    "min": 626.0,
                    "25%": 645.5,
                    "50%": 658.5,
                    "75%": 668.75,
                    "90%": 704.7,
                    "99%": 710.37,
                    "max": 711.0
                },
                "output_tokens": {
                    "mean": 217.1,
                    "std": 113.064829,
                    "min": 76.0,
                    "25%": 136.75,
                    "50%": 180.5,
                    "75%": 311.75,
                    "90%": 361.5,
                    "99%": 397.95,
                    "max": 402.0
                },
                "total_tokens": {
                    "mean": 879.7,
                    "std": 133.263357,
                    "min": 702.0,
                    "25%": 796.0,
                    "50%": 838.5,
                    "75%": 992.0,
                    "90%": 1023.2,
                    "99%": 1097.72,
                    "max": 1106.0
                },
                "total_input_tokens": 6626,
                "total_output_tokens": 2171,
                "total_tokens_count": 8797
            },
            "ttft": {
                "mean": 0.941571,
                "std": 0.002361,
                "min": 0.93671,
                "25%": 0.941331,
                "50%": 0.942036,
                "75%": 0.942454,
                "90%": 0.943032,
                "99%": 0.945262,
                "max": 0.94551
            },
            "tpot": {
                "mean": 0.029029,
                "std": 0.000704,
                "min": 0.027566,
                "25%": 0.028712,
                "50%": 0.029203,
                "75%": 0.029561,
                "90%": 0.029633,
                "99%": 0.029726,
                "max": 0.029736
            }
        }
    },
    "num": 10
}