Productizing LLMs

LLM(Large Language Models) demonstrate remarkable capabilities, but from an engineering standpoint they present very challenging problems. Replit explains the process of productizing LLMs based on the know-how gained from deploying models of various sizes, ranging from 100 million to over 100 billion parameters.

To address the problem of LLMs generating nonsensical content or repeating the same phrases, the following control methods are utilized:

• Temperature: Adjusts the model's randomness to control the predictability of the output.
• Frequency Penalty: Adjusts the probability that the model will select a word it has previously generated again.
• Cycle detection: When the model falls into a loop, the output is truncated or intervention is made through the prompt.
• Sampling: Samples outputs at various temperatures to select the optimal result.
• Layering another model: Generates multiple candidates and then uses another model to select the best answer.
• Training & Fine-tuning: Trains the model to prevent repetition or fine-tunes it based on user feedback.

In addition, to prevent performance regression during model updates, quality is continuously tracked using standard benchmarks such as HumanEval. Alongside this, the frequency with which the model falls into loops and the number of times it generates nonsensical responses are monitored, and changes to the model are validated through A/B testing.