Memory
RoPE scaling changes how rotary position frequencies or positions are mapped during inference. It can let compatible models admit positions beyond the range represented during training.
Longer admitted inputs expand the workloads a model can handle without changing its base weights. Quality can decline outside the trained range, and scaling methods behave differently.
A server applies the scaling method expected by the model configuration and tokenizer behavior. Teams validate long-context tasks, short-context quality, and cache capacity before deployment.
RoPE scaling changes how rotary position frequencies or positions are mapped during inference. It can let compatible models admit positions beyond the range represented during training. A server applies the scaling method expected by the model configuration and tokenizer behavior. Teams validate long-context tasks, short-context quality, and cache capacity before deployment.
Longer admitted inputs expand the workloads a model can handle without changing its base weights. Quality can decline outside the trained range, and scaling methods behave differently.
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