fineinstructions/query_templatizer
The fineinstructions/query_templatizer is a 1 billion parameter causal language model developed by fineinstructions. This model is specifically designed to convert natural language queries, instructions, or prompts into a generic, instruction template in the JSON format of FineTemplates. It excels at abstracting specific details from user inputs to create reusable, structured templates, making it ideal for tasks requiring query generalization and structured data extraction from free-form text.
Loading preview...
Overview
This model, developed by fineinstructions, is a 1 billion parameter causal language model engineered to transform natural language queries into structured, generic instruction templates. It outputs a JSON object containing the templatized query, following the FineTemplates format. This process involves identifying and replacing specific details within a user's input with generic placeholders, making the query reusable and adaptable for various applications.
Key Capabilities
- Query Templatization: Converts detailed user queries into abstract, templated JSON structures.
- Structured Output: Generates a JSON object that includes the
templatefield, acompatible_document_description, and various metadata tags likeqa_or_tasky,realistic,conversational,task_type_open,task_type_closed,difficulty,compatibility,query_frequency, and boolean flags foris_knowledge_recall,is_reasoning,is_personal_life, etc. - Contextual Understanding: Extracts the essence of a query to create a generalized template while retaining the core intent.
Use Cases
- Automated Prompt Engineering: Streamline the creation of diverse prompts from a single template.
- Data Augmentation: Generate synthetic datasets by filling templates with various specific examples.
- Search and Retrieval Systems: Standardize user queries for more effective information retrieval.
- Instruction Generation: Create generalized instructions for other language models or automated systems.
This model was trained using a synthetic dataset generated with DataDreamer 🤖💤, emphasizing its ability to handle diverse and complex query structures.