Particula-JSON eliminates the most frustrating problem in LLM integration: unreliable structured outputs. While general-purpose models frequently produce malformed JSON, miss required fields, or violate schemas, Particula-JSON is trained specifically for structured data generation. The result is near-perfect JSON validity and schema compliance at a fraction of the cost.
How this model is built
Three engineering choices that make a 7B model outperform 175B generalists on its task.
Schema-First Training
Trained on millions of schema/instance pairs, with the schema in the context every time. The model learns shape, not just text.
Constrained Decoding
Token sampling is gated by the schema at inference. Malformed JSON is mathematically impossible — not just unlikely.
Production Validation
Every release runs against a 50K-case regression set before it ships to inference. No quiet quality regressions.