How Model Distillation Enhances LLM Performance on the TLM Platform

Model Distillation Overview

Knowledge Distillation (KD) transfers knowledge from a high‑capacity teacher model to a smaller student model. The student learns from the teacher’s soft label probability distribution, which encodes inter‑class relationships and uncertainty, in addition to the hard ground‑truth labels.

Softmax with Temperature

Given logits z_i for class i, the teacher computes a softened probability distribution using a temperature T>1:

The same temperature is applied to the student logits p_i so that both distributions are comparable.

Distillation Loss

The divergence between teacher and student soft distributions is measured with the Kullback‑Leibler (KL) divergence. The loss is scaled by T² to keep the magnitude stable during back‑propagation:

Total Loss

The overall training objective combines the distillation loss with the standard cross‑entropy loss on true labels (temperature T=1) weighted by a balance factor α:

Practical Distillation on the TLM Platform

The TLM (Large Model Development) platform provides a UI for creating a distillation task. Users select a teacher model, a student model, a dataset, and optional hyper‑parameters. The platform launches the training job, monitors GPU/CPU/container usage, and streams logs.

After training, the platform generates an evaluation report that compares teacher and student performance (e.g., accuracy, latency) and visualizes the trade‑off.

For reference, the TLM platform can be accessed at https://zyun.360.cn/product/tlm.