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README.md

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 Welcome to the llmware HuggingFace page.   We believe that the ascendence of LLMs creates a major new application pattern and data 
 pipelines that will be transformative in the enterprise, especially in knowledge-intensive industries.    Our open source research efforts 
 are focused both on the new "ware" ("middleware" and "software" that will wrap and integrate LLMs), as well as building high-quality
-automation-focused enterprise RAG models.  
+automation-focused enterprise Agent, RAG and embedding models.  
 
-Our latest initiative is the SLIM model portfolio (Structured Language Instruction Models) - check out how to use in multi-model, Agent-based workflows - [SLIM-Video](https://www.youtube.com/watch?v=cQfdaTcmBpY) | [SLIM-Examples-Code](https://github.com/llmware-ai/llmware/tree/main/examples/SLIM-Agents/) 
+Our model training initiatives fall into four major categories:
+
+--SLIMs (Structured Language Instruction Models) - small, specialized function calling models for stacking in multi-model, Agent-based workflows  
+
+--BLING/DRAGON - highly-accurate fact-based question-answering models  
+
+--Industry-BERT - industry fine-tuned embedding models  
+
+--Private Inference Self-Hosting, Packaging and Quantization - GGUF, ONNX, OpenVino  
 
 
 Please check out a few of our recent blog postings related to these initiatives:   
+  [SMALL MODEL ACCURACY BENCHMARK](https://medium.com/@darrenoberst/best-small-language-models-for-accuracy-and-enterprise-use-cases-benchmark-results-cf71964759c8) | 
+  [OUR JOURNEY BUILDING ACCURATE ENTERPRISE SMALL MODELS](https://medium.com/@darrenoberst/building-the-most-accurate-small-language-models-our-journey-781474f64d88) | 
+  [THINKING DOES NOT HAPPEN ONE TOKEN AT A TIME](https://medium.com/@darrenoberst/thinking-does-not-happen-one-token-at-a-time-0dd0c6a528ec) | 
+  [SLIMs](https://medium.com/@darrenoberst/slims-small-specialized-models-function-calling-and-multi-model-agents-8c935b341398) | 
   [BLING](https://medium.com/@darrenoberst/small-instruct-following-llms-for-rag-use-case-54c55e4b41a8) | 
   [RAG-INSTRUCT-TEST-DATASET](https://medium.com/@darrenoberst/how-accurate-is-rag-8f0706281fd9) | 
   [LLMWARE EMERGING STACK](https://medium.com/@darrenoberst/the-emerging-llm-stack-for-rag-deee093af5fa) |