Dictionary
Model Distillation
Training a small model to mimic a large one.
1 min readupdated 2026-07-04
/ quick answer
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.
/ frequently asked
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.
↳ connected nodes
Dictionary↳ linked
Fine-Tuning
Continuing to train a base model on your own examples to specialize its behavior.
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RLHF
Reinforcement learning from human feedback, the alignment technique behind ChatGPT.
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DPO (Direct Preference Optimization)
A simpler alternative to RLHF that skips the reward model.
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LoRA
Low-Rank Adaptation: cheap fine-tuning that patches only small matrices.
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MCP (Model Context Protocol)
Open protocol that lets LLMs connect to tools, data sources and apps through a standard interface.
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Multimodal Model
A model that reads and reasons across text, images, audio, and video.