book-sft-pipeline

Automate ePub-to-JSONL dataset creation and LoRA fine-tuning.

225|71|Updated Jan 9, 2026
One-click install
npx skills add https://github.com/hoangnb24/skills --skill book-sft-pipeline-hoangnb24
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: book-sft-pipeline
Source: https://github.com/hoangnb24/skills/tree/main/skills/book-sft-pipeline
Command: npx skills add https://github.com/hoangnb24/skills --skill book-sft-pipeline-hoangnb24

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Book SFT Pipeline automates turning books into fine-tuning datasets and training style-transfer models, enabling author-voice style adaptation from literature.

Core Features & Use Cases

  • End-to-end ePub text extraction and conversion to training-ready chunks
  • Intelligent segmentation into 150-400 word blocks with overlap for robust style transfer
  • Diverse instruction generation using multiple system prompts and templates
  • JSONL dataset construction and LoRA fine-tuning integration on Tinker
  • Suitable for small-to-medium datasets aiming at author-voice replication

Quick Start

Provide an ePub book to start generating 150-400 word training chunks and 2 variants per chunk for a LoRA fine-tuning run.

Frequently Asked Questions about book-sft-pipeline

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

FAQPage Schema
How do I create an SFT dataset from an ePub book for LoRA fine-tuning?▼

To create an SFT dataset from an ePub for LoRA fine-tuning, the pipeline extracts text, segments it into 150-400 word blocks with overlap, generates diverse prompts, and outputs a JSONL file ready for training.

What is the best way to segment book text for author-style transfer tasks?▼

The best way to segment book text for author-style transfer is using paragraph-based segmentation that creates 150-400 word blocks with overlap, ensuring robust style replication across training chunks.

Can I use this pipeline for small-to-medium dataset fine-tuning workflows?▼

Yes, this pipeline is explicitly suitable for small-to-medium dataset fine-tuning workflows, applying LoRA rank 32 to a base model like Qwen/Qwen3-8B-Base for author-voice replication.

Does the pipeline support diverse instruction generation for SFT datasets?▼

Yes, the pipeline supports diverse instruction generation by applying multiple system prompts and templates to each segmented chunk, producing multiple training variants for LoRA fine-tuning.

What base models are compatible with this ePub to SFT dataset pipeline?▼

The pipeline is compatible with base models such as Qwen/Qwen3-8B-Base, applying LoRA rank 32 for fine-tuning to achieve author-style transfer from the extracted ePub text.

How do I prepare an ePub file for JSONL dataset construction?▼

To prepare an ePub file for JSONL dataset construction, provide the ePub to the pipeline which handles text extraction, paragraph-based segmentation, prompt generation, and outputs the final JSONL dataset.