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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 designing 
-automation-focused enterprise RAG models, including both industry-specific embedding models and instruction-focused generation models.
+automation-focused enterprise RAG models.
 
 Please check out a few of our blog postings related to these initiatives:   
   [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) | 
   [MODEL SIZE TRENDS](https://medium.com/@darrenoberst/are-the-mega-llms-driving-the-future-or-they-already-in-the-past-c3b949f9f5a5) |
-  [OPEN SOURCE RAG](https://medium.com/@darrenoberst/open-source-llms-in-rag-89d397b39511) |
+  [OPEN SOURCE RAG](https://medium.com/@darrenoberst/open-source-llms-in-rag-89d397b39511)  
   [1B-3B-7B LLM CAPABILITIES](https://medium.com/@darrenoberst/rag-instruct-capabilities-they-grow-up-so-fast-2647550cdc0a)