LoRA Adapter Merging — Combine Adapters for Multi-Task Models

I will implement LoRA adapter merging for your multi-task fine-tuning scenario — combining multiple task-specific LoRA adapters through weight interpolation or task arithmetic, producing a single model that retains the improvements of each adapter without requiring multiple separate model deployments. LoRA adapter merging is most valuable when you have independently fine-tuned adapters for different tasks and want a single deployment model that serves all tasks — rather than maintaining separate deployments and routing between them, adapter merging produces a single model checkpoint that combines the improvements from all adapters.

The implementation covers adapter interpolation strategy selection (linear interpolation, TIES merging, or DARE), merging coefficient optimisation, multi-task evaluation confirming each adapter's task improvement is preserved, merged model quality comparison against individual adapters, and delivery of the merged model checkpoint.