Programming Language
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LMQL is a robust programming language specifically designed for Large Language Models (LLMs). It is tailored to facilitate effective interaction with these models by offering modular prompting capabilities using types, templates, constraints, and an optimizing runtime. This allows users to employ LMQL for a wide range of tasks, from simple queries to complex procedural programming tasks. One of the standout features of LMQL is its support for nested queries, which enables the creation of modularized local instructions. This enhances prompt component reuse and facilitates procedural programming within the prompting environment.
Additionally, LMQL ensures portability across different backends, allowing users to switch between them seamlessly with a single line of code. Prompt construction and generation in LMQL are implemented using expressive Python control flow and string interpolation, providing users with a flexible and powerful tool for interacting with LLMs. Whether you are creating prompts with types, templates, and constraints for various tasks or implementing procedural programming tasks by leveraging LMQL's modularized local instructions, LMQL offers a comprehensive solution. It also ensures seamless portability across different backends and platforms for LLM interaction, making it a versatile tool for ML engineers, data scientists, AI developers, and researchers.
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Training and optimization of language models
Generation of coherent and contextually appropriate text
Fine-tuning of language models on specific tasks
Model evaluation and analysis for performance improvement
Deployment and integration of language models into applications
Create prompts for LLMs with types, templates, and constraints for various tasks.
Implement procedural programming tasks by leveraging LMQL's modularized local instructions.
Ensure seamless portability across different backends and platforms for LLM interaction.
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