Key features
- Zero parse errors: APAC guaranteed valid JSON via schema-driven structural token filling
- Any LLM: APAC works with smaller APAC-language models that have weak instruction following
- JSON Schema: APAC full type system — objects, arrays, strings, numbers, booleans, enums
- No retry logic: APAC eliminate JSON validation try/except retry in APAC data pipelines
- HuggingFace: APAC drop-in wrapping of any HuggingFace model for structured output
- Nested schemas: APAC complex nested JSON schemas for APAC domain entity extraction
Best for
- APAC engineering teams extracting structured data from Japanese, Korean, and Chinese text using smaller or domain-specialist LLMs with inconsistent instruction following — particularly APAC organizations running fine-tuned domain models where JSON schema compliance cannot be reliably achieved through prompting alone, and where zero parsing failures are required for downstream pipeline reliability.
Limitations to know
- ! APAC requires access to HuggingFace model logits — does not work with cloud API-only LLMs
- ! APAC complex nested schemas add inference overhead as more schema tokens require LLM calls
- ! APAC semantic validation (value ranges, enum membership) requires additional application logic
About Jsonformer
Jsonformer is an open-source structured generation library that provides APAC engineering teams with guaranteed valid JSON output from HuggingFace language models — implementing a generation strategy where the library provides all structural JSON tokens (braces, brackets, colons, commas, quotes) directly from the JSON schema definition and instructs the LLM to generate only the value tokens that fill the schema. Because the LLM never generates structural JSON tokens, it is structurally impossible to produce malformed JSON regardless of the LLM's instruction-following capability.
Jsonformer's schema-driven generation approach is particularly valuable for APAC teams using smaller or less instruction-tuned APAC-language models — a 7B Japanese-instruction model may inconsistently produce valid JSON with complex nested schemas when prompted with free-form instructions, but Jsonformer guarantees valid JSON from the same model because structural correctness is enforced by the generation architecture rather than the model's prompt comprehension. APAC teams using fine-tuned domain specialist models (Japanese legal, Korean financial, Chinese medical) that prioritize domain knowledge over instruction-following quality use Jsonformer to extract structured data reliably from these models.
Jsonformer's Python API wraps any HuggingFace model with a simple schema-driven interface — APAC teams pass the JSON schema (as a Python dict following JSON Schema format), the input text or prompt, and receive a guaranteed-valid Python dict output with no parsing step required. APAC data pipeline teams replacing `json.loads(llm_output)` try/except patterns with Jsonformer eliminate the retry logic, fallback prompts, and output validation code that accumulates in extraction pipelines built on unconstrained LLM generation.
Jsonformer supports the full JSON Schema type system including nested objects, arrays, strings, numbers, booleans, and enums — APAC extraction pipelines can specify complex nested schemas for Japanese corporate disclosure extraction, Korean e-commerce product attribute extraction, or Chinese financial statement field extraction with full type fidelity guaranteed in the output. APAC teams requiring specific value constraints beyond JSON type (like enum membership or numeric range validation) use Jsonformer for structural guarantee and add lightweight application-layer validation for semantic constraints.
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