book-sft-pipeline

You can create a supervised fine-tuning dataset from an ePub by extracting text, segmenting it into semantically coherent chunks, and generating diverse synthetic instructions. This pipeline handles ePub ingestion, intelligent segmentation, and Tinker-compatible dataset construction automatically.

To train an LLM to mimic an author's style, you use a multi-stage pipeline that generates diverse instruction-response pairs from source books and applies LoRA training. This approach ensures the model learns stylistic patterns rather than memorizing content.

The best way to prevent content memorization during LoRA style transfer training is to use diverse synthetic instruction generation with multiple prompt templates and system prompts. This variation forces the model to learn the underlying style instead of repeating specific text.

Yes, you can use your own ePub files for LLM training. The pipeline includes text extraction from ePubs and intelligent segmentation that splits long texts into semantically coherent chunks of 150-400 words suitable for fine-tuning.

Yes, the fine-tuning pipeline explicitly supports Tinker-compatible dataset construction. It processes extracted book text through segmentation and synthetic instruction generation to produce datasets ready for Tinker training environments.

A limitation of using synthetic instruction generation for author emulation is that the quality depends on the original ePub text and the diversity of prompt templates. Overfitting can still occur if the source material lacks stylistic variety across the segmented chunks.