SteamPunk Knowledge Distiller

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Current Operation: Knowledge Extraction

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Distilled Knowledge

Core Insight

Knowledge distillation is the process of transferring knowledge from a large, complex model (teacher) to a smaller, more efficient one (student) while maintaining performance. This enables deployment on resource-constrained devices.

Key Components

Teacher Model: Large, accurate but computationally expensive
Student Model: Smaller, faster but needs to learn from teacher
Distillation Loss: Combines hard targets and soft probabilities
Temperature Parameter: Controls softmax smoothness