Empirical evaluation of 13 quantization configurations on 6 LLMs for APR shows reduced memory (up to 85%) but increased inference time/energy, different repaired problem sets with little overlap, and 48% of configs strictly dominated.
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Smaller Models, Unexpected Costs: Trade-offs in LLM Quantization for Automated Program Repair
Empirical evaluation of 13 quantization configurations on 6 LLMs for APR shows reduced memory (up to 85%) but increased inference time/energy, different repaired problem sets with little overlap, and 48% of configs strictly dominated.