{
  "request": {
    "method": "POST",
    "url": "http://localhost:11434/api/generate",
    "headers": {},
    "body": {
      "model": "llama3.2:3b",
      "raw": true,
      "prompt": "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nTest metrics generation 0<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
      "options": {
        "temperature": 0.0
      },
      "stream": false
    },
    "endpoint": "/api/generate",
    "model": "llama3.2:3b"
  },
  "response": {
    "body": {
      "__type__": "ollama._types.GenerateResponse",
      "__data__": {
        "model": "llama3.2:3b",
        "created_at": "2025-08-11T15:56:06.703788Z",
        "done": true,
        "done_reason": "stop",
        "total_duration": 2722294000,
        "load_duration": 9736083,
        "prompt_eval_count": 21,
        "prompt_eval_duration": 113000000,
        "eval_count": 324,
        "eval_duration": 2598000000,
        "response": "Here are some test metrics that can be used to evaluate the performance of a system:\n\n1. **Accuracy**: The proportion of correct predictions made by the model.\n2. **Precision**: The ratio of true positives (correctly predicted instances) to total positive predictions.\n3. **Recall**: The ratio of true positives to the sum of true positives and false negatives (missed instances).\n4. **F1-score**: The harmonic mean of precision and recall, providing a balanced measure of both.\n5. **Mean Squared Error (MSE)**: The average squared difference between predicted and actual values.\n6. **Mean Absolute Error (MAE)**: The average absolute difference between predicted and actual values.\n7. **Root Mean Squared Percentage Error (RMSPE)**: A variation of MSE that expresses the error as a percentage.\n8. **Coefficient of Determination (R-squared, R2)**: Measures how well the model explains the variance in the data.\n9. **Mean Absolute Percentage Error (MAPE)**: The average absolute percentage difference between predicted and actual values.\n10. **Mean Squared Logarithmic Error (MSLE)**: A variation of MSE that is more suitable for skewed distributions.\n\nThese metrics can be used to evaluate different aspects of a system's performance, such as:\n\n* Classification models: accuracy, precision, recall, F1-score\n* Regression models: MSE, MAE, RMSPE, R2, MSLE\n* Time series forecasting: MAPE, RMSPE\n\nNote that the choice of metric depends on the specific problem and data.",
        "thinking": null,
        "context": null
      }
    },
    "is_streaming": false
  }
}