{
  "request": {
    "method": "POST",
    "url": "http://localhost:11434/api/generate",
    "headers": {},
    "body": {
      "model": "llama3.2:3b-instruct-fp16",
      "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-instruct-fp16"
  },
  "response": {
    "body": {
      "__type__": "ollama._types.GenerateResponse",
      "__data__": {
        "model": "llama3.2:3b-instruct-fp16",
        "created_at": "2025-08-11T15:51:12.918723Z",
        "done": true,
        "done_reason": "stop",
        "total_duration": 8868987792,
        "load_duration": 2793275292,
        "prompt_eval_count": 21,
        "prompt_eval_duration": 250000000,
        "eval_count": 344,
        "eval_duration": 5823000000,
        "response": "Here are some common test metrics used to evaluate the performance of a system:\n\n1. **Accuracy**: The proportion of correct predictions or classifications out of total predictions made.\n2. **Precision**: The ratio of true positives (correctly predicted instances) to the sum of true positives and false positives (incorrectly predicted instances).\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)**: The square root of the mean of the squared percentage differences between predicted and actual values.\n8. **Coefficient of Determination (R-squared, R2)**: Measures how well a model fits the data, with higher values indicating better fit.\n9. **Mean Absolute Percentage Error (MAPE)**: The average absolute percentage difference between predicted and actual values.\n10. **Normalized Mean Squared Error (NMSE)**: Similar to MSE, but normalized by the mean of the actual values.\n\nThese metrics can be used for various types of data, including:\n\n* Regression problems (e.g., predicting continuous values)\n* Classification problems (e.g., predicting categorical labels)\n* Time series forecasting\n* Clustering and dimensionality reduction\n\nWhen choosing a metric, consider the specific problem you're trying to solve, the type of data, and the desired level of precision.",
        "thinking": null,
        "context": null
      }
    },
    "is_streaming": false
  }
}