Knowledge Distillation

A compression technique that reduces the numerical precision of a trained model's weights and activations from FP32 to INT8 or INT4. Unlike knowledge distillation, which transfers behavioral patterns, quantization directly reduces the bit-width of parameters. This is often applied after distillation to further compress the student model for edge deployment. Key methods include:

• Post-Training Dynamic Quantization: Calibrates scaling factors on a small representative dataset.
• Quantization-Aware Training (QAT): Simulates quantization noise during training to recover accuracy lost during direct compression.

Low-Rank Adaptation is a parameter-efficient fine-tuning method that injects trainable low-rank matrices into a frozen pre-trained model. While knowledge distillation trains a separate compact model, LoRA adapts the original model by updating only a fraction of parameters. For genomic models, LoRA allows rapid domain adaptation to specific species or sequencing platforms without full fine-tuning. The technique drastically reduces GPU memory requirements by freezing the original weights and only optimizing the injected rank-decomposition matrices.

A memory-saving technique that trades compute for memory by discarding intermediate activations during the forward pass and recomputing them during the backward pass. This is critical when training a teacher model on long genomic sequences where memory constraints would otherwise force smaller batch sizes. Unlike distillation, which reduces inference cost, checkpointing reduces training memory footprint. It is often combined with ZeRO Optimization to train billion-parameter genomic foundation models on commodity GPU clusters.

A method that uses both FP16 and FP32 formats during model training to reduce memory footprint and accelerate computation on modern GPU architectures. While knowledge distillation addresses model size, mixed precision addresses training speed. For genomic transformer models, this technique halves memory usage while preserving the dynamic range needed for stable gradient updates. It relies on a loss scaling factor to prevent underflow in small gradient values during backpropagation.

The continuous monitoring process that identifies when a deployed student model's predictive performance degrades due to changes in the underlying genomic data distribution. After distilling a teacher model for production, drift detection ensures the compressed model maintains accuracy as sequencing technologies evolve. Key monitoring signals include:

• Data Drift: Statistical divergence between training and production variant distributions.
• Concept Drift: Changes in the relationship between genomic features and predicted phenotypes.
• Prediction Drift: Shifts in the model's output distribution over time.

An inference serving technique that dynamically appends new sequence requests to a running batch, maximizing GPU utilization. After distilling a genomic model, continuous batching ensures the compact student model achieves maximum throughput in production. Unlike static batching, which waits for a full batch, this method immediately processes incoming variant calling or expression prediction requests. It is a core feature of serving frameworks like vLLM and Triton Inference Server.