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  ---
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- # Triplex
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- <!-- Provide a quick summary of what the model is/does. -->
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- Triplex is a model for creating knowledge graphs from unstructured data developed by [SciPhi.AI](https://www.sciphi.ai).
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- It works by extracting triplets - simple statements consisting of a subject, predicate, and object - from text or other data sources.
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- Try the demo here: [kg.sciphi.ai](https://kg.sciphi.ai)
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- ## Model Details
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- It is a finetuned version of Phi3-3.8B on a high quality proprietary dataset constructed using DBPedia, Wikidata, and other data sources.
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- ### Model Sources
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  - **Blog:** [https://www.sciphi.ai/blog/triplex](https://www.sciphi.ai/blog/triplex)
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  - **Demo:** [kg.sciphi.ai](kg.sciphi.ai)
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- - **R2R Repository:** [https://www.github.com/SciPhi-AI/R2R](https://www.github.com/SciPhi-AI/R2R)
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  ```python
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  import json
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  ---
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+ # Triplex: a SOTA LLM for knowledge graph construction.
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+ Knowledge graphs, like Microsoft's Graph RAG, enhance RAG methods but are expensive to build. Triplex offers a 98% cost reduction for knowledge graph creation, outperforming GPT-4 at 1/60th the cost and enabling local graph building with SciPhi's R2R.
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+ Triplex is a finetuned version of Phi3-3.8B for creating knowledge graphs from unstructured data developed by [SciPhi.AI](https://www.sciphi.ai). It works by extracting triplets - simple statements consisting of a subject, predicate, and object - from text or other data sources.
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/668d8d7a2413acbd544530d1/kcUC5FDEoziMSEcjVHQ3-.png)
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+ ## Benchmark
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/668d8d7a2413acbd544530d1/xsZ2UPZE5mnTFvgAsQwtl.png)
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+ ## Usage:
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  - **Blog:** [https://www.sciphi.ai/blog/triplex](https://www.sciphi.ai/blog/triplex)
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  - **Demo:** [kg.sciphi.ai](kg.sciphi.ai)
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+ - **Cookbook:** [https://r2r-docs.sciphi.ai/cookbooks/knowledge-graph](https://r2r-docs.sciphi.ai/cookbooks/knowledge-graph)
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  ```python
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+
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  import json
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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