LMQL

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LMQL

LMQL: A Programming Language for Large Language Models

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About LMQL

LMQL is a groundbreaking programming language specifically designed for large language models (LLMs). Leveraging types, templates, constraints, and an optimizing runtime, LMQL provides robust and modular prompting systems for developers working with LLMs. This innovative approach not only simplifies the process of generating and optimizing prompts but also ensures high accuracy and efficiency, empowering users to harness the full potential of language models for various applications. With LMQL, developers can define precise output formats, enforce logical constraints, and utilize advanced features like nested queries and template-based generation, enabling the creation of sophisticated and reliable LLM-driven solutions. Created by the SRI Lab at ETH Zurich, LMQL is at the forefront of language model research and development, offering powerful tools for both novice and expert users.

Key Features

  • Nested Queries
  • Scripted Prompting
  • Custom Constraints
  • Optimizing Runtime
  • Playground IDE
  • Local Model Support
  • Tool Augmentation
  • High-level Constraint Management
  • Sequential Query Execution
  • Integration with Popular Libraries

Tags

programming languagelarge language modelstypestemplatesconstraintsoptimizing runtimequeriesSRI LabETH Zurichnested queriesscripted promptingcustom constraintsPlayground IDE

FAQs

What is LMQL?
LMQL is a programming language designed for large language models, enabling robust and modular prompting through types, templates, and constraints.
Who developed LMQL?
LMQL was developed by the SRI Lab at ETH Zurich along with various contributors.
Can LMQL handle nested queries?
Yes, LMQL supports nested queries, which allow for modularized local instructions and the re-use of prompt components.
What is scripted prompting in LMQL?
Scripted prompting in LMQL allows for control flow and branching behavior in prompt construction, enabling more sophisticated prompt designs.
What constraints can be applied in LMQL?
LMQL allows for high-level constraints such as length restrictions and logical conditions to be applied to prompt outputs.
Is there an IDE available for LMQL?
Yes, LMQL provides a Playground IDE which can be used for writing and testing queries.
Can I use local models with LMQL?
Yes, LMQL supports self-hosted models via frameworks like 🤗 Transformers and llama.cpp.
What are some use cases of LMQL?
LMQL can be used for complex prompt generation, chatbots, interactive systems, and any scenario requiring structured prompting and constrained generation.
Does LMQL support integration with other tools?
Yes, LMQL supports tool augmentation and has integrations with LangChain, LlamaIndex, and Pandas among others.
How does LMQL optimize LLM queries?
LMQL uses an optimizing runtime to efficiently handle constraints and improve the performance of large language model queries.