dataset-engineering

What problem does it solve?

This Skill streamlines the end-to-end process of creating, cleaning, and augmenting datasets for LLM fine-tuning. It helps data teams produce high-quality data faster, reducing noise and inconsistencies that degrade model performance.

Core Features & Use Cases

• Dataset formats: Alpaca, ShareGPT, ChatML, and custom schemas to fit your training pipelines.
• Data generation & augmentation: Create synthetic examples, paraphrase, back-translation, and difficulty variation to expand coverage.
• Quality & governance: Deduplicate, filter low-quality samples, standardize formats, and integrate with HuggingFace datasets.
• Use Case: Prepare a medical Q&A dataset in Alpaca format, include diverse topics, and publish a dataset-ready JSON file and a HuggingFace card.

Quick Start

Install dependencies, prepare your example data in Alpaca format, and run the transformation pipeline to produce a clean, training-ready dataset.