The application provides a
code reading large model parameter optimization method based on vllm deployment, which is implemented by a computer, first, the core
viewpoints of a paper are combined, an experimental group is designed, a benchmark experimental group and an
experimental control group are designed, experimental
record data contents are used, experimental results are obtained, and mathematical analysis is combined to analyze the obtained data, then, suspected problem data is investigated and surveyed to obtain the best parameter configuration of the
code reading large model relying on the vllm deployment, the core
viewpoints of the paper can be combined to design reasonable benchmark experimental groups and
experimental control groups, experimental
record data contents are used, and mathematical analysis is combined to obtain the best parameter practice of the
code reading large model relying on the vllm deployment. The application can ensure that the parameter optimization result of the code reading large model deployed on the vllm has a complete evidence chain, and has enough confidence to apply the optimized parameters in a real production environment.