Originally published on AI Tech Connect.
What model merging actually solves Say you have fine-tuned the same base model twice: once on a coding dataset, once on a customer-support transcript set. Each fine-tune is good at its own job and mediocre at the other. The obvious fix is to deploy both and route requests between them, but that means two GPUs, two sets of weights to keep patched, and a routing layer to maintain. Model merging offers a third option — combine the two sets of fine-tuned weights into a single checkpoint that inherits capability from both, with no additional training run. The idea rests on a simple observation: a fine-tune is just its base model plus a delta, usually called a task vector — the difference between the fine-tuned weights and the pre-trained weights they started from. If two fine-tunes share the…
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