book-sft-pipeline

What problem does it solve?

This skill automates the process of converting books into datasets suitable for training language models to mimic specific authorial styles, addressing the challenge of creating high-quality, style-transferable training data from long-form text.

Core Features & Use Cases

• Intelligent Segmentation: Splits books into semantically coherent chunks (150-400 words) at natural boundaries.
• Diverse Instruction Generation: Creates varied prompts to prevent model memorization and encourage style learning.
• LoRA Training: Facilitates fine-tuning base models using LoRA on platforms like Tinker for efficient style adaptation.
• Use Case: You want to train a model to write poetry in the style of Emily Dickinson. This skill will process her collected works, generate training examples, and guide the fine-tuning process.

Quick Start

Use the book-sft-pipeline skill to fine-tune a model on the provided ePub file to replicate the author's style.