The invention relates to the technical field of
chemical engineering and
natural language processing, in particular to an efficient parameter
fine tuning method for a
large model in the
chemical engineering field. According to the method, aiming at the defect that'fine-tuning
granularity ', 'parameter flexibility' and'
resource efficiency 'are difficult to consider in an existing parameter efficient fine-tuning technology, a double-Hadamard-product mechanism is introduced to realize fine-
granularity adjustment of
model parameters, and key parameters in a chemical task are dynamically identified and optimized in combination with an adaptive parameter
selection strategy; therefore, a high-efficiency
fine tuning frame with low parameter quantity and high precision is constructed. According to the method, finer-grained model
adaptation can be realized under the condition that only a small number of parameters are trained, and important parameters are adaptively selected according to the complexity of different chemical tasks, so that the
fine tuning performance is ensured, and the parameter utilization efficiency and the task
adaptation flexibility are remarkably improved.