What size models are best for this pipeline?

This pipeline is specifically optimized for small, efficient models (8B parameters or less), such as Qwen-8B or Llama-3-8B, which can be effectively tuned using LoRA adapters on a single book's data.

What is the benefit of using a base model over an instruct model?

Base models are more malleable and lack the rigid conversational constraints of instruction-tuned models, making it significantly easier to overlay new stylistic patterns without resistance from previous tuning.

How does the pipeline prevent the model from simply memorizing the plot?

It uses synthetic instruction generation where an LLM describes a scene without quoting it. This forces the training process to focus on 'how' things are said (the style) rather than just 'what' is said (the content).

Why use ePub instead of PDF for training data?

ePub provides structured HTML/XML tags that allow for clean paragraph extraction. PDFs often contain OCR errors and inconsistent line breaks that an AI model might mistakenly learn as stylistic patterns.

Book SFT & Style Transfer Pipeline

Name: Book SFT & Style Transfer Pipeline
Author: Kalyanikhandare29

byKalyanikhandare29

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Data Science & ML

Builds high-quality fine-tuning datasets from literary works to train AI models in specific authorial voices and writing styles.

The Book SFT Pipeline provides a comprehensive framework for transforming raw ePub files into optimized Supervised Fine-Tuning (SFT) datasets. It automates the complex process of semantic text segmentation, synthetic instruction generation, and LoRA training configuration, specifically targeting small base models like Qwen-8B. By emphasizing diverse prompt templates and intelligent chunking over raw content memorization, this skill enables developers to create AI agents that capture the unique rhythm, vocabulary, and stylistic markers of any author for creative writing or specialized content generation.

Key Features

01Synthetic instruction generation using 15+ diverse prompt templates to prevent overfitting.

020 GitHub stars

03Automated ePub text extraction preserving paragraph-level structures.

04Intelligent semantic segmentation with paragraph-aware overlap for coherence.

05Ready-to-use LoRA training configurations optimized for the Tinker platform.

06Advanced validation framework including modern scenario testing and originality verification.

Use Cases

01Converting long-form literary content into structured JSONL training pairs for 8B models.

02Training a creative writing assistant to mimic a specific literary style or authorial voice.

03Generating high-quality synthetic text datasets for research in linguistic style transfer.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add kalyanikhandare29/agent-skills-for-context-engineering book-sft-pipeline

For use in Claude.ai and ChatGPT

Download Skill

Key Features

01Synthetic instruction generation using 15+ diverse prompt templates to prevent overfitting.

020 GitHub stars

03Automated ePub text extraction preserving paragraph-level structures.

04Intelligent semantic segmentation with paragraph-aware overlap for coherence.

05Ready-to-use LoRA training configurations optimized for the Tinker platform.

06Advanced validation framework including modern scenario testing and originality verification.

Use Cases

01Converting long-form literary content into structured JSONL training pairs for 8B models.

02Training a creative writing assistant to mimic a specific literary style or authorial voice.

03Generating high-quality synthetic text datasets for research in linguistic style transfer.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add kalyanikhandare29/agent-skills-for-context-engineering book-sft-pipeline

For use in Claude.ai and ChatGPT

Download Skill