book-sft-pipeline

You can fine-tune a language model to replicate an authorial style by converting books into SFT datasets and applying LoRA training to base models. Base models are prioritized because they offer better malleability for absorbing new stylistic patterns.

Generate diverse instructions from long-form text by applying multiple prompt templates and system prompts to semantically coherent text chunks. This variation prevents model memorization and encourages the model to learn the underlying authorial style instead of specific phrases.

Yes, you can fine-tune base models using LoRA to replicate author voice. LoRA facilitates efficient style adaptation on platforms like Tinker, prioritizing base models over instruction-tuned models for better style malleability.

The best way to chunk long-form text for Supervised Fine-Tuning is splitting books into 150-400 word segments at natural boundaries. This intelligent segmentation ensures chunks remain semantically coherent for accurate style learning.

Yes, the pipeline works directly with ePub files for dataset generation by handling text extraction, intelligent segmentation, and diverse instruction generation. It converts the extracted book text into JSONL format suitable for LoRA training.