I already created a medical graph dictionary with nodes and edges, generated uniform graph vectors (85%) and combined them with MiniLLM vectors (15%) and utilized successfully in MLM and CLM (preidict next token) training. With only 500 Pubmed data sam…
Below code was utilized create unform graph vectors based on nodes and edges of a medical graph dictionary with 500 nodes (body parts, cellular structure, diseases, medical treatment, symptoms), hierarchical order (parent, child) and medical relationsh…
Medical SLM Model Output based on Graph Dictionary, 85% to 100% token success, 0.002 loss, 1.01 perplexity and all of this based on only 500 PubMed dataset samples and 85% weight on graph dictionary vector embeddings, These are simply results of 20 epo…
