Quantization & LoRA

Low-Rank Adaptation (ΔW = B·A), rank selection, FP8/INT4 uniform quantization, and scale-zero point math.

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Explanation

A focused 3-minute explanation using the topic's authored material.

Learning goals and prerequisites

After this lesson

Implement low-rank adaptation and basic quantization
Measure quality-efficiency tradeoffs
Choose an adaptation and deployment strategy

Helpful before starting

Linear layers and matrix decomposition
Numeric precision and neural-network training
LLM architecture and evaluation

Start here

Quantization & LoRA, in plain language

Low-Rank Adaptation (ΔW = B·A), rank selection, FP8/INT4 uniform quantization, and scale-zero point math. Quantization and low-rank adaptation reduce memory, compute, and training cost, but require careful calibration and task-specific quality evaluation.

For a small example, map four floating values to a tiny integer range. Choose scale and zero point, quantize and dequantize, then calculate the introduced error. This is the mechanism to keep in view as the lesson becomes more technical. Before moving on, identify the input, transformation, output, and one observation that would falsify your conclusion.

Key points

Full fine-tuning vs adapters and the low-rank update idea.
Numeric formats, range, scale, zero point, rounding, and saturation.
Weight-only vs weight-activation quantization and PTQ vs QAT.

Use it well

When Quantization & LoRA helps—and where it breaks

A model needs domain style on limited hardware. Train LoRA adapters on curated data, compare with prompting, evaluate regressions, and test quantized inference quality and memory. A useful result still depends on checking the assumptions and evidence below rather than treating one successful output as proof.

Key points

Assuming a bit width predicts real speed. Better approach: Benchmark supported kernels and memory traffic on target hardware.
Selecting LoRA rank from convention. Better approach: Run rank and target-module ablations.
Calibrating on a tiny unrepresentative sample. Better approach: Cover realistic domains, lengths, and activation outliers.

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