Model Overview

The fineinstructions/template_instantiator is a 3.2 billion parameter causal language model developed by fineinstructions. Its primary function is to automate the creation of instruction and answer pairs by instantiating templates from the FineTemplates dataset using a given document.

Key Capabilities

• Instruction Template Instantiation: Generates a specific instruction and its corresponding answer based on a generic template and a provided document.
• Synthetic Data Generation: Produces structured JSON output containing the instantiated instruction and answer.
• Contextual Excerpt Expansion: Features a helper function to expand <excerpt> tags within the generated answer, replacing them with relevant text segments from the input document, ensuring contextual accuracy.

How it Works

The model processes a JSON input containing an instruction_template and a document. It then generates an output JSON object with the instantiated instruction and answer. A crucial post-processing step involves using a Python helper function to locate and expand <excerpt> placeholders in the generated answer, drawing content directly from the original document. This process is powered by a synthetic dataset created with DataDreamer 🤖💤.

Good For

• Dataset Creation: Ideal for researchers and developers looking to generate large-scale, high-quality synthetic instruction-following datasets.
• Fine-tuning Data Preparation: Useful for preparing diverse and contextually rich training examples for other language models.
• Automated Content Generation: Can be adapted for tasks requiring the generation of structured Q&A pairs from unstructured text.