Edge Distillation with Multi-step Tuning Pipeline for SLMs

Engineer a high-fidelity SLM for interactive persona by distilling linguistic patterns from frontier models (GPT 5.4).

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Primary Features

Distill latent reasoning and Chain-of-Thought (CoT) capabilities from GPT-5.4 into a 3B model.
Engineer multi-step tuning pipeline - SFT for grounding, RKD for logic, and DPO for stylistic parity.
Standardize input/output schemas using chat templates.
Implement 4-bit quantization (GGUF) to balance VRAM efficiency and perplexity for edge hardware.
Deploy via AWS SageMaker LMI/vLLM engine for paged-attention concurrency and real-time streaming.

Digital_Clone_ver1.0

Architected by Kuriko IWAI

Kuriko IWAI

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Related Books for Further Understanding

These books cover the wide range of theories and practices; from fundamentals to PhD level.

Linear Algebra Done Right

Linear Algebra Done Right

Foundations of Machine Learning, second edition (Adaptive Computation and Machine Learning series)

Foundations of Machine Learning, second edition (Adaptive Computation and Machine Learning series)

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

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Share What You Learned

Kuriko IWAI, "Edge Distillation with Multi-step Tuning Pipeline for SLMs" in Kernel Labs

https://kuriko-iwai.com/labs/digital-clone-edge-distillation

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Written by Kuriko IWAI. All images, unless otherwise noted, are by the author. All experimentations on this blog utilize synthetic or licensed data.