Knowledge Distillation: How to Train Small Models from Large Ones
A technical deep-dive into knowledge distillation and how we use it to create production-ready Small Language Models.
Knowledge distillation is a technique for transferring knowledge from a large "teacher" model to a smaller "student" model. In this post, we'll explore how it works and why it's particularly effective for production LLM use cases.
What is Distillation?
At its core, distillation involves training a smaller model to mimic the behavior of a larger model. The key insight is that the larger model's outputs contain more information than just the final answer—they encode the model's "confidence" across all possible outputs.
Why Distillation Works
Large language models are trained on massive, general datasets. But in production, you're usually solving a much narrower problem. A support chatbot doesn't need to know how to write poetry or solve calculus problems.
By distilling on your specific use case, we create a model that's:
The OpenTracy Approach
We've developed several innovations that make distillation practical:
Trace-Based Training
Instead of synthetic data, we use your actual production traces. This ensures the model learns from real examples.
Automated Curation
Not all traces are equal. We automatically filter for high-quality examples that will improve model performance.
Continuous Evaluation
We continuously evaluate the distilled model against the teacher, ensuring quality is maintained.
Results
Our approach consistently achieves 95%+ quality retention while reducing model size by 10-100x.