Problem

MISSING: Pruning is a critical model compression technique that removes unnecessary weights/neurons to reduce model size and inference cost.

Existing

• Issue Quantization – PTQ and QAT (INT8/INT4) with LoRA Compatibility #278: Quantization (different technique)
• LoRA adapters (low-rank, different technique)

Missing Implementations

Unstructured Pruning (HIGH):

• Magnitude-based pruning (remove smallest weights)
• Gradient-based pruning
• Movement pruning (mask learning)
• Global vs layer-wise thresholds

Structured Pruning (CRITICAL):

• Channel pruning (entire filters)
• Neuron pruning
• Head pruning (for transformers)
• Block pruning

Advanced Techniques (MEDIUM):

• Lottery Ticket Hypothesis (find winning subnetworks)
• Iterative pruning (gradual removal)
• Fine-tuning after pruning
• One-shot pruning vs iterative

Metrics:

• Sparsity ratio
• FLOPs reduction
• Memory reduction
• Accuracy degradation

Use Cases

• Deploy large models on edge devices
• 50-90% parameter reduction with minimal accuracy loss
• Faster inference
• Lower memory footprint

Architecture

Success Criteria

• Prune BERT to 50% sparsity with <1% accuracy loss
• Prune ResNet to 70% sparsity
• Integration with training loop
• Benchmarks on standard datasets