On a private driving-scenario test set, a pipeline combining dynamic prompts, synthetic data, distillation with LoRA, and AWQ quantization raises average accuracy of a 7B vision-language model from 0.542 to 0.894.
Generative adversarial networks,
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Research on Driving Scenario Technology Based on Multimodal Large Lauguage Model Optimization
On a private driving-scenario test set, a pipeline combining dynamic prompts, synthetic data, distillation with LoRA, and AWQ quantization raises average accuracy of a 7B vision-language model from 0.542 to 0.894.