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Dictionary

Model Distillation

Training a small model to mimic a large one.

1 min readupdated 2026-07-04

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Distillation uses a big model to label a dataset, then trains a small model on those labels. Produces fast, cheap models that keep 80-95% of teacher quality on the target task. Training a small model to mimic a large one.

Training a small model to mimic a large one. Distillation uses a big model to label a dataset, then trains a small model on those labels. Produces fast, cheap models that keep 80-95% of teacher quality on the target task. In practice: Distill GPT-4 outputs on your support tickets into a fine-tuned Llama-3-8B — 20x cheaper, near-parity. This dictionary node is part of the Onexial knowledge graph and links to related concepts, workflows and tools below.
Definition
Distillation uses a big model to label a dataset, then trains a small model on those labels. Produces fast, cheap models that keep 80-95% of teacher quality on the target task.
Example
Distill GPT-4 outputs on your support tickets into a fine-tuned Llama-3-8B — 20x cheaper, near-parity.
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What is Model Distillation?

Distillation uses a big model to label a dataset, then trains a small model on those labels. Produces fast, cheap models that keep 80-95% of teacher quality on the target task.

What is an example of Model Distillation?

Distill GPT-4 outputs on your support tickets into a fine-tuned Llama-3-8B — 20x cheaper, near-parity.

Why does Model Distillation matter for AI and automation?

Training a small model to mimic a large one. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.

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Related concepts

The vocabulary this page depends on.

  • Fine-Tuning

    Continuing to train a base model on your own examples to specialize its behavior.

  • RLHF

    Reinforcement learning from human feedback, the alignment technique behind ChatGPT.

  • DPO (Direct Preference Optimization)

    A simpler alternative to RLHF that skips the reward model.

  • LoRA

    Low-Rank Adaptation: cheap fine-tuning that patches only small matrices.

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Related workflows

Turn this into a repeatable process.

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Related tool stacks

The tools that run it in production.

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Comparisons & alternatives

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