Lmql AI

Explore the power of LMQL 0.7, the programming language tailored for LLMs developed by SRI Lab at ETH Zurich, featuring robust prompting with types, templates, constraints, and an optimizing runtime.

Features

• Nested Queries: LMQL introduces nested queries to your LLM prompting, allowing for modular programming and reusable prompt components, increasing efficiency and maintainability.
• Cross-Backend Compatibility: Ensure your LLM code is portable across multiple backends with ease, using LMQL's ability to switch environments with just a single line of code.
• Versatile Prompting Toolkit: Empower your development with tools like constrained LLMs, types and regex, multi-part prompts, and more for a sophisticated and controlled prompting experience.
• Seamless Python Integration: LMQL's Python support enables developers to harness the familiarity and power of Python for prompt construction with rich control flow and string interpolation.

Use Cases:

• Procedural Programming for LLMs: Leverage procedural constructs within your prompts to create complex, dynamic interactions with LLMs enabled by LMQL's nested queries.
• Streamlined Development Across Multiple Platforms: Build your LLM applications in LMQL and run them on various platforms such as llama.cpp, OpenAI, and Hugging Face Transformers without changing your code base.
• Advanced Prompt Engineering: Utilize LMQL's advanced prompt engineering capabilities for mission-critical tasks that require precision, like tool augmentation and chatbot development.

LMQL 0.7 is a ground-breaking update offering programmers unprecedented control, modularity, and portability for LLM prompting, thus setting a new standard in language model interaction.