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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 Agent, RAG and embedding models.  
+automation-focused enterprise Agent, RAG and embedding small specialized language models.  
 
 Our model training initiatives fall into four major categories:
 
-**SLIMs** - small, specialized function calling models for stacking in multi-model, Agent-based workflows  
-
+**SLIMs** - small, specialized function calling models for stacking in multi-model, Agent-based workflows -- [SLIMs](https://medium.com/@darrenoberst/slims-small-specialized-models-function-calling-and-multi-model-agents-8c935b341398)   
 **BLING/DRAGON** - highly-accurate fact-based question-answering models  
-
+  -- [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)  
 **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) | 
-  [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)  
-  [1B-3B-7B LLM CAPABILITIES](https://medium.com/@darrenoberst/rag-instruct-capabilities-they-grow-up-so-fast-2647550cdc0a)  
+  [BECOMING A MASTER FINETUNING CHEF](https://medium.com/@darrenoberst/6-tips-to-becoming-a-master-llm-fine-tuning-chef-143ad735354b)  
   
 Interested?  [Join us on Discord](https://discord.gg/MhZn5Nc39h)