This parameter in vLLM dictates the utmost enter sequence size the mannequin can course of. It’s an integer worth representing the very best variety of tokens allowed in a single immediate. As an example, if this worth is about to 2048, the mannequin will truncate any enter exceeding this restrict, guaranteeing compatibility and stopping potential errors.
Setting this worth appropriately is essential for balancing efficiency and useful resource utilization. The next restrict allows the processing of longer and extra detailed prompts, doubtlessly enhancing the standard of the generated output. Nonetheless, it additionally calls for extra reminiscence and computational energy. Selecting an acceptable worth entails contemplating the standard size of anticipated enter and the obtainable {hardware} sources. Traditionally, limitations on enter sequence size have been a serious constraint in giant language mannequin functions, and vLLM’s structure, partially, addresses optimizing efficiency inside these outlined boundaries.
