book-sft-pipeline

You can convert an ePub book into a training-ready SFT dataset by extracting clean text, segmenting it into coherent 150-400 word chunks, and generating diverse instructions formatted as JSONL for fine-tuning.

Yes, you can fine-tune a small LoRA adapter on a base model using a generated JSONL dataset to capture a specific authorial style and enable style-transfer training.

The best way to segment long-form novels for fine-tuning is breaking the text into coherent 150-400 word chunks with overlap, maximizing data efficiency while preserving context for instruction generation.

Yes, the pipeline supports generating multiple prompts and system prompts during dataset construction to encourage robust style learning and prevent model memorization of the original text.

You validate an author-style model trained from a book by checking the validation artifacts produced at the end of the LoRA training pipeline to ensure the output accurately reflects the target style.

Yes, you need Tinker for the LoRA training pipeline, as the outputted JSONL datasets are specifically formatted for LoRA-style fine-tuning with Tinker to capture the target authorial styles.