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genpark-knowledge-distillation-teacher-student-tracker-skill

mcp
Guvenlik Denetimi
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SUMMARY

GenPark AI Agent Skill - Knowledge distillation teacher-student loss engine with temperature-scaled Kullback-Leibler divergence for continual model compression and capability preservation.

README.md

GenPark Knowledge Distillation Teacher Student Tracker Skill

Teacher-student knowledge distillation loss engine with temperature-scaled soft targets and KL divergence.

Explore more frameworks at GenPark and the GenPark MCP Catalog.

graph TD
    A[Teacher Model] -->|Logits / T| B[Soft Targets Q]
    C[Student Model] -->|Logits / T| D[Soft Predictions P]
    B & D --> E[Distillation Loss = T^2 * KL(Q || P)]
    C -->|Logits / 1.0| F[Hard Cross-Entropy Loss]
    E & F --> G[Total L = (1-alpha)*L_hard + alpha*L_distill]

Features

  • Temperature scaling parameterization.
  • Analytical KL divergence computation.
  • Pure Python standard library.

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