Despite　their　potential,　the　industrial　deployment　of　large　language　models　(LLMs)　is　constrained　by　traditional　fine-tuning　procedures　that　are　both　resource-intensive　and　time-consuming.　Low-Rank　Adaptation　(LoRA)　has　emerged　as　a　pioneering　methodology　for　addressing　these　challenges.　By　integrating　low-rank　decomposition　matrices　into　network　weights　to　reduce　trainable　parameters,　LoRA　effectively　accelerates　the　adaptation　process.　While　research　on　LoRA　primarily　focuses　on　adjusting　low-rank　matrices,　DyLoRA　optimizes　the　rank-setting　mechanism　to　avoid　extensive　effort　in　rank　size　training　and　searching.　However,　DyLoRA　rank　configuration　mechanism　has　its　own　limitation.　First,　DyLoRA　sets　the　same　rank　size　for　all　the　low-rank　adaptation　layers　at　each　time　step.　Given　that　layers　with　different　depth　contain　distinct　information,　they　should　have　varying　rank　values　to　accurately　capture　their　unique　characteristics.　Second,　the　truncated　phase　selected　for　ordering　representation　based　on　nested　dropout　regulation　is　only　half　dynamic,　continuously　dropping　tail　units,　thereby　limiting　its　ability　to　access　information.　In　this　work,　we　propose　a　novel　technique,　enhanced　range　adaptation　in　time　and　depth　aware　dynamic　LoRA　(ERAT-DLoRA)　to　address　these　problems.　The　ERAT-DLoRA　method　introduces　a　dynamic　range　to　the　truncated　phase　that　makes　the　truncated　phase　fully　dynamic.　Additionally,　we　design　a　time　and　layer-aware　dynamic　rank　to　ensure　appropriate　adjustments　at　different　time　steps　and　layer　levels.　We　evaluate　our　solution　on　natural　languages　understanding　and　language　generation　tasks.　Extensive　evaluation　results　demonstrate　the　effectiveness　of　the　proposed　method.