eth-sri/lmql

Features

• Constraint-Based Prompting - Provides a framework for forcing LLM responses to follow strict formatting and data types via logit masking.
• Real-time Generation Constraints - Forces model responses to adhere to strict formats and data types using real-time token-level constraints and logit masking.
• Interleaved Logic Execution - Merges traditional programming control flow with stochastic text generation within a single execution runtime.
• Inference Optimizations - Reduces latency and increases throughput using specialized caching and asynchronous batching for model requests.
• Structured Output Generators - Forces language models to produce strictly typed, machine-readable data formats using guided prompting.
• Prefix Caching - Stores and reuses common prompt prefix computations to avoid redundant calculations across multiple variations.
• Logit Masks - Applies binary logit masks to the output distribution to disable specific tokens and enforce data type constraints.
• Constraint-Guided Decoders - Interleaves programmatic logic with token sampling to ensure generated text adheres to strict structural requirements.
• Probabilistic Programming Languages - Integrates traditional control flow with the probabilistic nature of large language model generation.
• LLM - Blends algorithmic logic with stochastic text generation to create a language for guiding and constraining LLM outputs.
• Programmatic Workflow Logic - Combines developer-written programmatic flow with LLM queries to control the reasoning and structure of generated output.
• Algorithmic - Reduces token usage and response times through speculative execution, constraint short-circuiting, and tree-based caching.
• LLM Response Streaming - Delivers model-generated text incrementally to clients via WebSockets or server-sent events for lower perceived latency.
• Beam Search Implementations - Implements beam search algorithms to track multiple probable token sequences for more accurate model responses.
• Decoding Strategy Implementations - Implements advanced token selection algorithms like beam search to optimize the quality and accuracy of generated text.
• Query Batching - Groups multiple computational queries into single execution calls to increase token throughput and reduce latency.
• Inference - Reduces token computation time through the combined use of tree-based caching and constraint short-circuiting.
• Batch Request Processing - Executes multiple requests in parallel using an asynchronous system to increase total processing throughput.
• Inference Short-Circuiting - Bypasses unnecessary model computations when constraints make specific token paths redundant or impossible.
• Application Frameworks - Programming language for large language models.
• Prompt Optimization Frameworks - Query language for programming complex prompt logic.
• Prompting Frameworks - Query language for programming and controlling language models.
• Structured Output Generation - Language for constraint-guided and efficient model programming.

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