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Distilled LLM Models Like Deepseek

Writer's picture: H Peter AlessoH Peter Alesso

Distilling a Large Language Model (LLM) refers to the process of transferring the “knowledge” (i.e., learned patterns, representations, and behaviors) from a large, complex model (often called the teacher) to a smaller, more efficient model (often called the student). The goal is to retain as much of the original model’s capabilities as possible (accuracy, fluency, reasoning skills, etc.) while significantly reducing the size and computational requirements.

Here’s how it generally works in practice:

  1. Teacher-Student Setup

    • You have a large, pre-trained model (the teacher) that performs well but might be slow or expensive to run in production.

    • You want a smaller model (the student) that can run faster or on devices with limited resources, yet still provide high-quality outputs.

  2. Soft Targets / Output Distributions

    • Instead of just using the ground-truth labels (for classification tasks) or raw text data (for language modeling), you use the teacher’s outputs—often the probability distributions over possible next words—to guide the student.

    • This information-rich output from the teacher is called a “soft target” or “soft label,” which the student tries to imitate.

  3. Knowledge Distillation Loss

    • The student model is trained by comparing its predictions to the teacher’s predictions (soft labels), rather than just to true labels or next tokens.

    • This encourages the student model to learn the teacher’s internal decision boundaries or “concepts” more effectively than if it only had hard labels (e.g., correct/incorrect token).

  4. Temperature Parameter

    • Often, a temperature parameter is used in the softmax function for the teacher’s outputs.

    • A higher temperature “softens” the teacher’s probability distribution, revealing more about how the teacher ranks various possible outputs. This typically helps the student learn more nuanced information.

  5. Benefits of Distillation

    • Smaller, faster model: The student has far fewer parameters, leading to less memory usage and faster inference.

    • Better than naive compression: Because the student mimics the teacher’s nuanced outputs, it often outperforms a model of similar size trained from scratch on the original dataset.

In summary, distilling an LLM means training a smaller model to replicate the behavior of a larger, more powerful one—retaining much of its performance while being significantly more efficient in terms of speed and resource usage.

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