NuExtract-1.5-smol
| Property | Value |
|---|---|
| Parameter Count | 1.71B |
| Model Type | Text Generation / Information Extraction |
| License | MIT |
| Tensor Type | BF16 |
| Base Model | SmolLM2-1.7B |
What is NuExtract-1.5-smol?
NuExtract-1.5-smol is a specialized language model designed for structured information extraction tasks. It's a fine-tuned version of SmolLM2-1.7B that maintains high performance while being more compact than its larger counterparts. This model is particularly notable for its multilingual capabilities and efficient architecture that enables processing of texts in multiple languages.
Implementation Details
The model leverages advanced architecture optimizations while maintaining a relatively small footprint of 1.71B parameters. It's implemented using BF16 tensor type for optimal performance and memory usage, and is designed to work with a JSON template-based extraction approach.
- Optimized for zero-shot performance across multiple languages
- Uses template-based extraction methodology
- Supports arbitrary sequence lengths through sliding window attention
- Recommended to use with temperature at or near 0 for optimal extraction
Core Capabilities
- Structured information extraction from unstructured text
- Multilingual support with strong zero-shot performance
- Template-based extraction using JSON schemas
- Efficient processing of long sequences
- Pure extraction focus with high accuracy in maintaining original text
Frequently Asked Questions
Q: What makes this model unique?
The model combines compactness (1.71B parameters) with powerful extraction capabilities, outperforming larger models in specific tasks while maintaining multilingual support. It's specifically optimized for pure extraction tasks, ensuring that generated content closely matches the source text.
Q: What are the recommended use cases?
The model excels in structured information extraction tasks where precise data needs to be pulled from unstructured text. It's particularly useful for automated data extraction, document processing, and multilingual information retrieval tasks where accuracy and efficiency are crucial.
