book-sft-pipeline

To create an SFT dataset from a book for LoRA training, you extract text from ePub or plain text files, segment it into coherent 150-400 word chunks with overlap, and generate diverse instructions to build JSONL datasets.

The best way to fine-tune a model for author style transfer is using a LoRA-based training flow on a structured SFT dataset, which teaches the model authorial style rather than memorizing passages.

Yes, you can use ePub files to generate Tinker-compatible JSONL datasets by extracting the text, segmenting it into narrative chunks, and constructing system, user, and assistant prompts suitable for training.

Text segmentation preserves narrative boundaries in book datasets by chunking extracted text into 150-400 word segments with overlap, ensuring the narrative flow is maintained for style-transfer instruction generation.

Validation checks prevent content leakage during style transfer training by running originality checks and modern-scenario tests, ensuring the fine-tuned model applies the authorial style without reproducing exact passages.

No, you do not need a large dataset to fine-tune a style-transfer model with LoRA, as the pipeline orchestrates fine-tuning on a small dataset to effectively capture the desired authorial voice.