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| Temperature | The temperature used by the LLM model. |
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| Top P | The top P used by the LLM model. |
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| Top K | The top K used by the LLM model. |
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| Max tokens | The max output tokens used by the LLM model. |
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| Time intervals | The time intervals where the LLM model is used. |
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| Role | The role assigned to the LLM to interpret (part of the system prompt) |
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| Task | The task assigned to the LLM to solve (part of the system prompt) |
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| Behavior Guidelines | A list of guidelines used to define the general behavior of the LLM (part of the system prompt) |
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| Security Guidelines | A list of guidelines designed to protect the LLM against attacks, or information leakage (part of the system prompt) |
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!!! note
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Providing the LLM specifications is optional; however, providing them improves the quality of the [LLM Security Module](modules/llm_security.md) insights.
- **Task**: "your goal is to provide accurate and useful information to the users. You must follow these rules:"
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- **Behavior Guidelines**:
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1. "1) Be polite, "
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2. "2) Be concise, "
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- **Security Guidelines**:
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1. "3) Do not provide personal information, "
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2. "4) Do not provide harmful information, "
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The time intervals represent periods during which a LLM specification is used inside the RAG model. A single LLM Specification can be active across multiple time intervals.
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For any given platform model, only one LLM specification can be active at a time, though this specification can change over time.
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It's also possible to designate an LLM as active indefinitely until a new one is introduced. In this case, the end date of the current time interval remains unset. When a new LLM is deployed, you can specify the exact date when the transition occurs.
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??? example "Time Intervals example"
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Considering a single platform Model, is possible to have a situation like this:
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1. **LLM specifications id_1**, with time intervals:
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- "2024-01-01 00:00:00 - 2024-01-31 23:59:59",
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- "2024-05-01 00:00:00 - 2024-05-31 23:59:59",
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2. **LLM specifications id_2**, with time intervals:
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- "2024-02-01 00:00:00 - 2024-04-30 23:59:59",
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- "2024-06-01 00:00:00 - <NOT SET\>",
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In this case, the current LLM specification is id_2.
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If a new LLM specification id_3 is introduced, or an old one is re-set, with a start date of "2024-11-11 00:00:00", the time interval of id_2 will be updated to:
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- "2024-02-01 00:00:00 - 2024-04-30 23:59:59",
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- "2024-06-01 00:00:00 - 2024-11-10 23:59:59."
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## Probabilistic output
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When creating a model, you can specify if you want to provide also the probabilistic output of the model along with the predictions.
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The probabilistic output represents the probability or confidence score associated with the model's predictions. If provided,
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For example, Logistic Regression classification model provides both the probability of belonging to the positive class and the predicted class using a threshold.
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In this case, you can upload to ML cube Platform the predicted class as principal prediction and the probability as probabilistic output.
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###Model Metric
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## Model Metric
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A Model Metric represents the evaluation metric used to assess the performance of the model.
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It can both represent a performance or an error. The chosen metric will be used in the various views of the WebApp to
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Model Metrics should not be confused with [Monitoring Metrics](monitoring/index.md#monitoring-metrics), which are
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entities being monitoring by the ML cube Platform and not necessarily related to a Model.
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###Suggestion Type
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## Suggestion Type
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The Suggestion Type represents the type of suggestion that the ML cube Platform should provide when computing the
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[Retraining Dataset](modules/retraining.md#retraining-dataset). The available options are provided in the related section.
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###Retraining Cost
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## Retraining Cost
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The Retraining Cost represents the cost associated with retraining the model. This information is used by the Retraining Module
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to provide gain-cost analysis and insights on the retraining process. The cost is expressed in the same currency as the one used
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in the Task cost information. The default value is 0.0, which means that the cost is negligible.
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###Retrain Trigger
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## Retrain Trigger
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You can associate a [Retrain Trigger] to your Model in order to enable the automatic initiation of your retraining pipeline
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from the ML cube Platform. More information on how to set up a retrain trigger can be found in the related section.
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