fine-tuning-data-generator

ChatML format synthetic data generation involves structuring multi-turn conversations as JSONL files with role-based message pairs. This Skill automates the end-to-end workflow: requirements gathering, generation planning, diverse example creation, strict JSONL formatting, and validation to produce deployment-ready datasets for Unsloth, Axolotl, and Hugging Face frameworks.

Yes. This Skill supports multiple domains including customer support, code generation, Q&A, technical writing, data analysis, and education. It tailors generation plans and multi-turn examples to your specific domain requirements, then outputs validated ChatML JSONL data ready for framework integration.

The Skill provides built-in validation tooling to verify ChatML JSONL formatting compliance, dataset structure integrity, and compatibility with target frameworks. Validation ensures your synthetic data meets quality standards before deployment to Unsloth, Axolotl, or Hugging Face training pipelines.

The Skill includes framework integration guidance and code examples for all three platforms. After generating and validating your ChatML JSONL dataset, follow the provided instructions to load data into your chosen framework and configure training parameters.

The workflow follows five stages: requirements gathering (define domain and task scope), generation-plan creation (specify data characteristics), diverse multi-turn example generation (produce examples), strict JSONL formatting (structure output), and validation (verify quality). This ensures systematic, high-quality synthetic dataset creation.

Yes. The Skill includes automated validation tooling that checks ChatML JSONL formatting, dataset metadata, and framework compatibility. Automation reduces manual QA effort and catches structural issues before training begins.