fineinstructions/query_templatizer
The fineinstructions/query_templatizer is a 1 billion parameter causal language model developed by fineinstructions, designed to convert natural language queries into generic, instruction templates. This model specializes in transforming user prompts into a structured JSON output following the FineTemplates format, making it ideal for standardizing and categorizing diverse user inputs. It excels at extracting key entities and structuring them into a reusable template, facilitating downstream processing and analysis of instructions. The model has a context length of 32768 tokens, supporting detailed and complex query templatization.
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Overview
The fineinstructions/query_templatizer is a 1 billion parameter causal language model specifically engineered to transform natural language queries, instructions, or prompts into a standardized, generic instruction template. This model outputs a JSON object formatted according to the FineTemplates schema, which includes a template field with placeholders for variable information and a compatible_document_description for potential document matching.
Key Capabilities
- Query Templatization: Converts diverse user inputs into a structured, reusable template format.
- JSON Output: Generates a comprehensive JSON object containing the templatized query and additional metadata.
- Metadata Generation: Provides fields such as
qa_or_tasky,realistic,conversational,task_type_open,difficulty, and various boolean flags (is_knowledge_recall,is_reasoning, etc.) to categorize the query. - High Context Length: Supports a context length of 32768 tokens, allowing for the processing of lengthy and complex queries.
Good For
- Standardizing User Inputs: Ideal for applications requiring consistent formatting of user queries for further processing or analysis.
- Dataset Creation: Useful for generating structured datasets from unstructured prompts, particularly for instruction-tuning other models.
- Query Categorization: The rich metadata in the JSON output can be used to classify queries based on their type, difficulty, and intent.
- Automated Prompt Engineering: Helps in creating generic prompt templates that can be filled with specific details for various tasks.
This model was trained using a synthetic dataset generated with DataDreamer 🤖💤, ensuring a robust and diverse training base for its specialized task.