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Chapter 4. Testing baseline model responses

Use the playground to test and evaluate your model’s baseline responses.

Prerequisites

  • You have created a playground for your deployed model.

Procedure

To test your model, follow these steps:

  1. From the OpenShift AI dashboard, click Gen AI studio Playground.
  2. From the model list, select the model that you want to test.

  3. Adjust the following model parameters as needed:

    1. Temperature: Control the randomness of the model’s output. Use values between 0 and 2.

      The temperature value directly influences creativity: * Values near 0 (0-0.3): Produce deterministic and factual responses, and are recommended for objective or factual tasks. * Values around 0.7: A common default for balanced output. * Values near 1 (0.7-1): Increase creativity and randomness, and are recommended for generative or creative tasks. * Values above 1 (such as 2): Typically produce incoherent output.

    2. Streaming: Show the LLM’s response as it is being generated. This is helpful for testing model latency and seeing the model’s progress in real time. When streaming is off, the full response will not render until it is complete.
    3. System instructions: Review or edit the text to define the context, persona, or instructions for the model. The playground provides a default prompt.
  4. In the chat input field, type a query.
  5. Click Send.

  6. Observe the model’s response.

    NOTE: After you send a prompt, the Send button in the chat input field changes to a Stop button. Click it if you want to interrupt the model’s response, for example, when the response takes longer than you anticipated or if you notice that you made an error in your prompt. The Bot posts “You stopped this message” to confirm your stop request.

  7. Optional. To clear the chat history and start a new conversation, click New Chat. The chat interface clears the chat history. Your playground configuration settings are preserved.

Verification

  • The model provides responses based on its general knowledge or pre-trained data.
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