about fine-tune using sdcup

may i use a bert-like model to load params of pre-train sdcup, then add some head top for task of table qa?

when i look into pre-train sdcup, can i ignore params like: "mlp_action1.linear.weight", "mlp_action1.linear.bias", "mlp_action2.linear.weight", "mlp_action2.linear.bias", "mlp_column1.linear.weight", "mlp_column1.linear.bias", "mlp_column2.linear.weight", "mlp_column2.linear.bias", "mlp_column1_single.linear.weight", "mlp_column1_single.linear.bias", "mlp_column2_single.linear.weight", "mlp_column2_single.linear.bias", "layer_norm_1.gamma", "layer_norm_1.beta", "layer_norm_2.gamma", "layer_norm_2.beta", "layer_norm_3.gamma", "layer_norm_3.beta". are these useful for fine-tune?

The issue names no files, tests, or entry points. Start by locating SDCUP's checkpoint-loading and fine-tuning entry points, then determine whether the listed parameters are required for table-QA fine-tuning and document the supported loading behavior.

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