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To generate valid JSON from text using a local LLM, you can use constrained generation. This process forces the model to output text that strictly adheres to a predefined schema, ensuring the resulting JSON is always structurally valid.

The best way to guarantee LLM output matches a Pydantic schema is through FSM-based constrained generation. This technique restricts token selection during decoding, ensuring the model only produces sequences that validate against your specific Python models.

Yes, you can use local model backends like vLLM, Transformers, and llama.cpp for structured text generation. This allows on-premises data processing without external API dependencies while enforcing schema adherence.

To extract structured data from product descriptions in Python, define a target schema and apply constrained generation to the text. This parses unstructured inputs like product names and prices directly into valid structured JSON objects.

FSM-based constrained generation restricts token sampling to enforce schema validity, which may impact generation speed. However, it eliminates post-processing validation errors, ensuring high accuracy for structured data tasks.

LLMs output invalid JSON because standard sampling allows invalid token sequences. Using FSM-based constrained generation restricts the output space during decoding, guaranteeing schema adherence and preventing malformed JSON syntax.