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Introduction:
Are you tired of struggling to interact with large language models (LLMs) using complex query languages?
Introducing LMQL, the ultimate solution for natural language querying of LLMs. With LMQL, you can effortlessly combine the power of Python with the simplicity of natural language prompts to enhance your interaction with LLMs.
Unlock a world of possibilities with LMQL’s extensive features, including constraints, debugging, retrieval, control flow, and seamless integration with 🤗 Transformers. Whether you need to tell a joke, generate a packing list, search Wikipedia, or chat with a bot, LMQL has got you covered with its pre-built prompts.
But that’s not all! LMQL goes beyond high-level constraints and allows you to programmatically control the generation process using regular Python control flow statements. It automatically generates tokens and validates the produced sequence, ensuring accuracy and efficiency.
With LMQL, you can even incorporate dynamic prompts and text processing through its support for arbitrary Python code in the prompt clause. The Scripted Beam Search feature further enhances the decoding process, exploring multiple possible answers.
Evaluate data sets effortlessly using Python’s assert, thanks to LMQL’s support for checking the correctness of generated output. Say goodbye to the complexities of working with LLMs and embrace the simplicity and efficiency of LMQL.
Experience the power of LMQL and revolutionize your interaction with LLMs. Are you ready to unleash the true potential of natural language querying?
Overview:
LMQL is a query language designed specifically for large language models (LLMs). It combines the natural language prompts with the expressiveness of Python to facilitate the interaction with LLMs.
The tool provides various features such as constraints, debugging, retrieval, control flow, and support for 🤗 Transformers, which make it easier to prompt responses from the LLM.
LMQL offers a broad range of pre-built prompts for tasks such as telling a joke, generating a packing list, searching Wikipedia, and chatting with a bot. In addition to providing high-level constraints, LMQL also allows users to control the generation process programmatically by supporting regular Python control flow statements.
The tool generates the required tokens automatically and validates the produced sequence as soon as the provided validation condition is definitively violated. LMQL also supports arbitrary Python code in the prompt clause, enabling dynamic prompts and text processing.
The Scripted Beam Search feature decodes the expert name and answer jointly, exploring multiple possible answers. LMQL supports Python’s assert to check the correctness of the generated output, which can be useful for evaluating data sets.
Overall, LMQL is a powerful tool that simplifies the interaction with LLMs and enables Python developers to work with natural language prompts more efficiently.
Benefits:
- LMQL is a query language designed specifically for large language models (LLMs).
- It combines the natural language prompts with the expressiveness of Python to facilitate the interaction with LLMs.
- The tool provides various features such as constraints, debugging, retrieval, control flow, and support for 🤗 Transformers, which make it easier to prompt responses from the LLM.
- LMQL offers a broad range of pre-built prompts for tasks such as telling a joke, generating a packing list, searching Wikipedia, and chatting with a bot.
- In addition to providing high-level constraints, LMQL also allows users to control the generation process programmatically by supporting regular Python control flow statements.
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