← Opening PreparationJul 2025

Clipping · Exhibit · Knowledge Distillation & Fine-Tuning

Pasted from the desk

SLM Distillation Engine

A knowledge distillation pipeline that transfers reasoning capabilities from a 70B parameter teacher model to a compact 3B SLM using QLoRA, DeepSpeed, and FlashAttention for efficient domain-specific fine-tuning.

Halftone photograph: two machines side by side, the smaller one cabled to the larger.
SLM Distillation — teacher and student machines, file photo.
11a233a44a567Fig.1.Fig.2.the column, sectioned

Reference

  1. 1.KETTLE — teacher traces — GraphRAG outputs
  2. 2.FURNACE — training compute
  3. 3.COLUMN — the distillation objective
  4. 4.CONDENSER — compression into the student
  5. 5.CASK — the 3B SLM
  6. 6.HYDROMETER — quality-parity eval
  7. 7.GAUGES — −50% latency · 12× inference

APPARATUS FOR THE DISTILLATION OF REASONING. Filed Apr. 2026.

11a233a44a567Fig.1.Fig.2.the column, sectioned

Tech

  • PyTorch
  • QLoRA
  • DeepSpeed
  • FlashAttention

The line

  1. 01

    Architected a knowledge distillation pipeline to transfer reasoning from a 70B teacher model to a 3B SLM, achieving 12x increase in inference speed.

  2. 02

    Implemented QLoRA for domain-specific fine-tuning on proprietary datasets for high-accuracy classification.

  3. 03

    Utilized DeepSpeed and FlashAttention to optimize training, reducing memory footprint by 40%.

  4. 04

    Designed a custom reward-modeling metric aligning the distilled outputs precisely with internal business rules, achieving a 98% pass rate on edge-cases.

Measurable impact

12x inference speedup

Repository← Opening Preparation