One Model, Many Roles: Specializing LLM Inference Without Training More Models
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I ran the same ten tool-selection tasks with four different tool catalogues against one shared model backend: 40 requests in total . With five tools, the requests used 6,208 input tokens in total. With fifty tools, they used 38,288 . Every condition returned the expected tool names and arguments, i…
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- 2026-09-29 18:30 · DEV Community — AI
One Model, Many Roles: Specializing LLM Inference Without Training More Models