Triplex
| Property | Value |
|---|---|
| Parameter Count | 3.82B |
| Model Type | Text Generation, Transformers |
| License | cc-by-nc-sa-4.0 |
| Tensor Type | BF16 |
What is Triplex?
Triplex is a specialized language model developed by SciPhi.AI, fine-tuned from Phi3-3.8B specifically for knowledge graph construction. This innovative model represents a significant breakthrough in automated knowledge extraction, offering comparable performance to GPT-4 at just 1/60th of the cost. The model excels at extracting triplets - structured representations consisting of subject, predicate, and object - from unstructured text data.
Implementation Details
The model is implemented using the transformers architecture and is available in both Safetensors and GGUF formats. It's designed to work with text-generation-inference endpoints and includes custom code for optimal performance. The implementation allows for easy integration through Python, with comprehensive support for named entity recognition (NER) and relationship extraction.
- Built on Phi3-3.8B architecture
- Optimized for BF16 tensor operations
- Supports batch processing of text inputs
- Includes custom tokenizer and model configurations
Core Capabilities
- Efficient knowledge graph construction from unstructured text
- Named Entity Recognition (NER) for multiple entity types
- Relationship extraction using predefined predicates
- 98% cost reduction compared to traditional methods
- Local graph building capability with SciPhi's R2R
Frequently Asked Questions
Q: What makes this model unique?
Triplex stands out for its exceptional efficiency in knowledge graph construction, offering near GPT-4 level performance at a fraction of the cost. It's specifically optimized for extracting structured information from unstructured text, making it ideal for large-scale knowledge graph projects.
Q: What are the recommended use cases?
The model is perfect for organizations needing to build knowledge graphs from large text datasets, research institutions requiring automated information extraction, and developers working on RAG (Retrieval-Augmented Generation) systems. It's particularly valuable for projects requiring cost-effective knowledge graph construction at scale.
