•1 min read•from Machine Learning
You can decompose models into a graph database [N]
Our take
Introducing a groundbreaking approach to model decomposition with the power of graph databases. By transforming static LLM models into graph structures, users can perform K-nearest neighbor (KNN) walks on each layer, achieving results mathematically identical to traditional matrix multiplication. This innovative method not only updates a model's internal knowledge without retraining—simply by inserting data into the graph database—but also optimizes memory usage.
https://github.com/chrishayuk/larql
https://youtu.be/8Ppw8254nLI?si=lo-6PM5pwnpyvwMXh
Now you can decompose a static llm model and do a knn walk on each layer (which was decomposed into a graph database), and it's mathematically identical to doing matmult. It allows you to update the models internal factual knowledge without retraining (just insert into graph DB), it also uses less memory (since its just a database). The creator is the CTO at IBM.
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