AI Training & Evaluation Dataset Generator
Synthesize multi-turn chat fine-tuning JSONL, DPO preference pairs, and RAG golden evaluation triples with customizable edge-case and adversarial distributions in standard OpenAI and HuggingFace formats.
Standard AI Dataset Formats Supported
Export synthetic datasets ready for immediate ingestion into OpenAI, HuggingFace, Axolotl, Unsloth, or Llama-Factory.
Multi-Turn Chat JSONL
Standard OpenAI format with system, user, and assistant turns. Ideal for fine-tuning conversational and customer service agents.
DPO Preference Pairs
Pairs of chosen vs. rejected responses with rationale for Direct Preference Optimization and RLHF alignment.
RAG Golden Eval Triples
question, retrieved_context, and ground_truth_answer for evaluating retrieval accuracy and faithfulness.
Alpaca Instruct Format
Classic instruction, input, and output tuples for supervised instruction fine-tuning across open-source models.
High-Diversity Synthetic Generation with Adversarial Stress Testing
Sylvaera eliminates repetitive data collapse by injecting semantic variation, diverse user personas, and complex edge cases. Use the adversarial slider to inject realistic prompt injection attempts, confusing user queries, and missing prerequisites to train models that are safe and resilient in production.