Create high-quality datasets for fine-tuning language models.
LoRA Dataset Curator
LoRA, PEFT, QLoRA, Axolotl frameworks
Best for
- ▸Building high-quality instruction datasets for domain-specific LoRA fine-tuning with 500-10,000 examples
- ▸Creating synthetic training data using stronger models while maintaining quality control and human validation
- ▸Designing seed examples and filtering pipelines for parameter-efficient fine-tuning of coding, writing, or reasoning tasks
- ▸Validating dataset quality using data-centric AI principles before expensive LoRA training runs
What you'll get
- ▸Complete dataset curation strategy with seed example templates, synthetic generation prompts, and quality scoring rubrics tailored to specific domain and model requirements
- ▸Structured quality filtering pipeline with automated checks, human review sampling protocols, and data validation frameworks optimized for parameter-efficient training
- ▸Domain-specific seed examples with negative cases, edge case coverage, and format specifications ready for scaling via self-instruct methodology
Clear fine-tuning objectives, target model specifications, example quality requirements, and constraints on dataset size, format, and domain coverage.
Structured dataset curation plan with seed examples, quality filtering criteria, synthetic data generation prompts, and validation frameworks ready for LoRA training.
What's inside
“[Step-by-step reasoning with clear logic, evidence, and conclusions] ``` **Diversity Injection**: Run generation multiple times with different prompts:”
Not designed for ↓
- ×Training datasets for full model pre-training or large-scale foundation model training
- ×Computer vision or multimodal dataset curation (focuses specifically on text-based LLM training)
- ×Real-time data collection or web scraping for training data
- ×Running the actual LoRA fine-tuning process or hyperparameter optimization
SupaScore
89.2▼
Evidence Policy
Standard: no explicit evidence policy.
Research Foundation: 8 sources (3 paper, 3 official docs, 1 web, 1 industry frameworks)
This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.
Version History
v5.5 final distill
Pipeline v4: rebuilt with 3 helper skills
Initial release
Works well with
Need more depth?
Specialist skills that go deeper in areas this skill touches.
Common Workflows
Complete LoRA Fine-tuning Pipeline
End-to-end workflow from dataset design through training to evaluation for domain-specific LoRA adapters
© 2026 Kill The Dragon GmbH. This skill and its system prompt are protected by copyright. Unauthorised redistribution is prohibited. Terms of Service · Legal Notice