Fig. 7: Data cleaning, fine-tuning, knowledge base construction, and fast inference framework. | npj Advanced Manufacturing

Fig. 7: Data cleaning, fine-tuning, knowledge base construction, and fast inference framework.

From: Leveraging large language models for efficient scheduling in Human–Robot collaborative flexible manufacturing systems

Fig. 7

a evolutionary data generation: The qwen2.5-coder-7b model is used with the SeEvo framework to run 50 rounds of evolution for each of the 200 random cases. The complete interaction logs from this process, including all successful evolutionary instructions and HDRs. b data cleaning and SFT: The raw data is filtered to retain only the lead to performance improvement. Specifically, if an offspring’s HDR outperforms its parent, the instruction-response pair that prompted this optimization is selected. These high-quality datasets are then used to perform LoRA fine-tuning on the qwen2.5-coder-7b. c HDRs knowledge base collection: The qwen2.5-sft-7b is employed to perform another 50 rounds of deep evolution on 20 training cases. The resulting evolved HDRs are collected to build a high-quality knowledge base, providing high-quality examples for the subsequent fast inference stage. d Fast HDR generation: the system calls the qwen2.5-sft-7b and utilizes the HDRs from the knowledge base as the initial individuals. With just a single iteration of the SeEvo framework, the system can generate a high-quality scheduling solution for a new problem within one minute.

Back to article page