Roses are red, violets are...

This visualization shows the probabilities for the top 9 most likely next tokens for the prompt "Roses are red, violets are ", and how those probabilities are affected by changing the temperature and top-k parameters.

Parameters

• Temperature: The temperature parameter controls the randomness of the predictions. A temperature of 1.0 is the default probability distribution generated by the model. When increasing the temperature, the distribution becomes more even - tokens that were less likely to be selected become more likely, and tokens that were more likely to be selected become less likely, which makes the output of the model more creative. Conversely, decreasing the temperature causes the tokens that were more likely to be selected to become even more likely, and tokens that were less likely to be even less likely, making the model output more predictable.
• Top-K: The top-k parameter controls the number of tokens to consider when generating the probabilities for the next token to be selected. A top-k of 1 will always return the most likely token, while a top-k of 9 will consider the top 9 most likely tokens.

While this visualization only shows the top 9 tokens for the prompt, in reality the large language model will return the likelyhood for thousands of tokens - 262144 in the case of Gemma 3 1B, the model used to generate the list of tokens.

Note that, while the data in this visualization was extracted from a a real LLM Prompt, the values are hard-coded, and there's no LLM running in the background. To learn more about how the data was generated using Gemma 3 - 1B, take a look at this Colab.