[Feature Request]: How to improve the accuracy of compressor for large SFT models through training

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Is the model that works best now llmlingua-2-xlm-roberta-large-meetingbank? If I want better results, can I combine it with the sft of a large model, such as adding such a compressor in front of the sft, so that the compressed text is sent to the large model for training. The data distribution obtained by such a large model is relatively consistent with the data distribution of the compressor, so that higher accuracy can be obtained in practical reasoning. Or if I directly use the data of the large model sft to continue training the compressor, will the compressor be better?

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