noamgat/lm-format-enforcer
Lm Format Enforcer
This library provides a framework for enforcing structural constraints on language model output during the token generation process. It functions as middleware that restricts model responses to strictly adhere to predefined JSON schemas or regular expression patterns, ensuring that generated text is machine-readable and consistent for downstream data consumption.
The project distinguishes itself by integrating directly with inference engines to intercept token probability distributions before the final sampling stage. By utilizing state-machine parsing and recursive schema decomposition, it performs lookahead validation to prune invalid token sequences. This approach allows for precise control over output, including the enforcement of specific field ordering within JSON objects and the ability to handle multiple concurrent generation streams through batched constraint execution.
The library supports a wide range of integration strategies, allowing it to function across diverse model backends and inference server environments. It includes diagnostic tools for analyzing the performance impact of these constraints, ensuring compatibility and efficiency across different hardware setups. The software is distributed as a Python package designed for integration into existing inference pipelines.
Features
- Structured Output Generators - Ensures language models produce text that strictly follows predefined JSON schemas or regular expression patterns.
- Output Constraint Engines - Restricts token generation during inference to ensure adherence to JSON schemas or regular expression patterns.
- Inference Middleware - Provides a standardized middleware layer to intercept and modify token probability distributions across diverse model backends.
- Output Constraint Engines - Enforces strict JSON schema adherence during token generation by intercepting probability distributions to prune invalid sequences.
- Schema Enforcement Tools - Validates and forces language model outputs to adhere to specific data structures during inference.
- Logit Masks - Filters model vocabulary at each generation step by masking tokens that violate structural constraints.
- LLM Output Constraints - Restricts language model token generation to strictly follow defined JSON schemas and regular expression patterns.
- Generation State Machines - Maintains a deterministic state machine to track schema positions and validate next-token candidates.
- Data Extraction Pipelines - Builds robust workflows for generating machine-readable data from language models.
- Regex Constraint Engines - Provides regex-based token masking to ensure generated text matches specific patterns for downstream data consumption.
- LLM Inference Engines - Connects output enforcement logic to various model execution backends.
- Batch Generation - Applies independent token constraints to multiple concurrent inputs during a single generation process.
- Regex-Based Output Filters - Limits language model output to match precise regular expression patterns for predictable data consumption.
- Parser Combinators - Merges distinct parsing logic into a unified workflow to process complex data structures.
- Generation Lookaheads - Prunes invalid token sequences by simulating potential future outputs to ensure structural compliance.
- Inference Engine Integrations - Provides a framework for applying structural output constraints across various model backends.
- Schema Decomposition - Decomposes complex data structures into manageable parsing rules evaluated incrementally during generation.
- Generation Stream Batching - Enables efficient handling of multiple concurrent generation streams by maintaining separate parser states for each request.
Star history
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