Model Merging

Merging differs from an ensemble because inference ordinarily uses one combined parameter set instead of consulting multiple models. Simple averaging interpolates weights; task-vector approaches combine differences from a shared base; other methods address redundant updates or conflicting signs. Compatibility involves architecture, parameter shapes, tokenizers and the relationship between training histories. A common base can make task updates more interpretable, but does not eliminate interference. TIES-Merging illustrates trimming small updates and resolving sign conflicts before combination. Merging can also apply to adapters, subject to the constraints of their representation and loading or conversion path.

Inspect architecture, licenses, base revisions and processing artifacts before selecting inputs. Choose the merge rule and coefficients on development tasks that represent all intended capabilities. Preserve separate final tests and compare the merged artifact with its base and each source model. Check language coverage, output formatting and regressions rather than using one aggregate score. Record the merge specification and reproduce it from pinned input checkpoints. The deliverable is a loadable model plus evidence of which capabilities survived, which changed and whether one merged artifact actually fits the deployment requirement.

Weights from unrelated architectures cannot be averaged meaningfully just because they serve similar tasks. Even compatible checkpoints may encode conflicting updates, and special-token differences can invalidate a merge. A merge can lose capability or amplify unwanted behavior without an obvious loading error. It does not create an inference ensemble's independent votes or a distillation student's learning process. Validate the complete output artifact and retain provenance of every input, since a compact merge recipe alone does not establish reproducible behavior.