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book-sft-pipeline

Automate language model fine-tuning pipelines for authorial style replication.

Updated Aug 23, 2026
Search Tags:#fine-tuning#LLM training#LoRA#author voice#style transfer#dataset generation
One-click install
npx skills add https://github.com/monmacllcapp/skill-forks --skill book-sft-pipeline-monmacllcapp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: book-sft-pipeline
Source: https://github.com/monmacllcapp/skill-forks/tree/main/skills/context-engineering/examples/book-sft-pipeline
Command: npx skills add https://github.com/monmacllcapp/skill-forks --skill book-sft-pipeline-monmacllcapp

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates the complex process of fine-tuning language models to replicate specific authorial styles from books, transforming raw text into a high-quality dataset for style transfer training.

Core Features & Use Cases

  • End-to-End Pipeline: Manages text extraction from ePubs, intelligent segmentation, diverse instruction generation, dataset construction, and LoRA training configuration.
  • Style Transfer Focus: Optimizes for learning an author's unique voice, rhythm, and vocabulary, rather than memorizing content.
  • Cost-Effective Training: Provides estimates and targets low costs for training smaller models (e.g., 8B parameters).
  • Use Case: Fine-tune a model to write like Shakespeare for generating new sonnets, or like Jane Austen for creating modern fan fiction in her style.

Quick Start

Use the book-sft-pipeline skill to fine-tune a model on the book 'Pride and Prejudice' to capture Jane Austen's writing style.

Frequently Asked Questions about book-sft-pipeline

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune an LLM to replicate an author's writing style from a book?▼

This pipeline automates fine-tuning LLMs to replicate authorial styles by managing text segmentation, diverse instruction generation, and LoRA training configuration. It transforms raw book text into a high-quality dataset optimized for style transfer rather than content memorization.

What is the best way to generate a training dataset from an ePub for style transfer?▼

The best approach uses intelligent text segmentation and diverse instruction generation with multiple prompt templates. The pipeline constructs datasets in JSONL format specifically optimized for teaching models an author's voice, rhythm, and vocabulary.

Can I use LoRA training to fine-tune smaller 8B parameter models on literary text?▼

Yes, LoRA training can fine-tune smaller 8B parameter models on literary text. This pipeline provides cost-effective training configurations and estimates, targeting low-cost training while enabling the model to adopt specific authorial voices.

How does style transfer for literary works differ from standard LLM fine-tuning?▼

Style transfer for literary works optimizes for learning an author's unique voice, rhythm, and vocabulary rather than memorizing content. This approach uses diverse instruction generation and multiple system prompts to ensure the model adopts the specific authorial voice.

Do I need to manually format training data when fine-tuning a model on books?▼

No, manual data formatting is not needed. The pipeline automates dataset construction in JSONL format, handling text extraction from ePubs, intelligent segmentation, and diverse instruction generation without manual formatting.

What are the limitations of using LoRA for author voice style transfer?▼

While LoRA enables cost-effective fine-tuning of smaller models, it may face limitations in fully replicating complex authorial voices. The pipeline mitigates this by using diverse instruction generation and multiple system prompts to optimize style transfer quality.

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