A Three-Phases SFT Hybrid Model Integrated Strong Prior Module and Data
  Overlap Estimation in the Eduation Context
Zhangquan Chen Chunjiang Liu Haobin Duan
In this paper, we propose an end-to-end prior-based three-phases supervised
fine-tuned model, which is proved more competitive than traditional fine-tuning
method. More specifically, our model realizes the structural disassembly and
incremental guided output of educational knowledge. To this end, we robustify
data classification of three types via a sampler and overlap estimation neural
network, and inject the preprocessing datasets into pre-trained model in three
batches for LORA fine-tuning. Then, we design a prior module couples system
prompt, vector databases, and abstract syntax tree task segmentation. Finally,
the compression method and regularization constraint are applied to the
prior-based fine-tuned model, followed by text filter at the output end to
obtain incremental guided results. Our model represents the first research
effort to truly embody the tutor role with the features of abundant educational
knowledge, step-by-step incremental guided outputs and non-disclosure of
answers. Extensive experiments report that our model also achieves
state-of-the-art in code abilities compared to open-source models, reaching an
impressive 75.10% on the HumanEval (@pass 1) benchmark. Additionally, our model
maintains strong conversational capabilities, with the 13B quantized version
achieving scores of 56.34, 50.60, and 45.27 respectively on the MMLU, C-Eval,
and AGIEval (5 shot) dialogue evaluation benchmarks.