This parameter in vLLM dictates the utmost enter sequence size the mannequin can course of. It’s an integer worth representing the best variety of tokens allowed in a single immediate. For example, if this worth is ready 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, probably enhancing the standard of the generated output. Nonetheless, it additionally calls for extra reminiscence and computational energy. Selecting an acceptable worth includes contemplating the everyday size of anticipated enter and the obtainable {hardware} assets. Traditionally, limitations on enter sequence size have been a serious constraint in massive language mannequin purposes, and vLLM’s structure, partially, addresses optimizing efficiency inside these outlined boundaries.
