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LMQL

Discover LMQL, a robust programming language designed for large language models, enabling modular, optimized prompting.

Other Updated 3 hours ago
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LMQL

LMQL's Top 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

Frequently asked questions about LMQL

LMQL is a programming language designed for large language models, enabling robust and modular prompting through types, templates, and constraints.

LMQL was developed by the SRI Lab at ETH Zurich along with various contributors.

Yes, LMQL supports nested queries, which allow for modularized local instructions and the re-use of prompt components.

Scripted prompting in LMQL allows for control flow and branching behavior in prompt construction, enabling more sophisticated prompt designs.

LMQL allows for high-level constraints such as length restrictions and logical conditions to be applied to prompt outputs.

Yes, LMQL provides a Playground IDE which can be used for writing and testing queries.

Yes, LMQL supports self-hosted models via frameworks like 🤗 Transformers and llama.cpp.

LMQL can be used for complex prompt generation, chatbots, interactive systems, and any scenario requiring structured prompting and constrained generation.

Yes, LMQL supports tool augmentation and has integrations with LangChain, LlamaIndex, and Pandas among others.

LMQL uses an optimizing runtime to efficiently handle constraints and improve the performance of large language model queries.

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Category
Other
Tags
programming language
large language models
types
templates
constraints
optimizing runtime
queries
SRI Lab
ETH Zurich
nested queries

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