Update README.md

README.md

@@ -33,56 +33,10 @@ Note: compare results with [dragon-mistral-7b](https://www.huggingface.co/llmwar
 <!-- Provide a longer summary of what this model is. -->
 
 - **Developed by:** llmware
-- **Model type:** bling-rag-instruct 
+- **Model type:** dragon-rag-instruct
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
-- **Finetuned from model:** Microsoft Phi-3
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-The intended use of BLING models is two-fold:
-
-1.  Provide high-quality RAG-Instruct models designed for fact-based, no "hallucination" question-answering in connection with an enterprise RAG workflow.
-
-2.  BLING models are fine-tuned on top of leading base foundation models, generally in the 1-3B+ range, and purposefully rolled-out across multiple base models to provide choices and "drop-in" replacements for RAG specific use cases.
-
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-BLING is designed for enterprise automation use cases, especially in knowledge-intensive industries, such as financial services,
-legal and regulatory industries with complex information sources.  
-
-BLING models have been trained for common RAG scenarios, specifically:   question-answering, key-value extraction, and basic summarization as the core instruction types
-without the need for a lot of complex instruction verbiage - provide a text passage context, ask questions, and get clear fact-based responses.
-
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-BLING models are designed to operate with grounded sources, e.g., inclusion of a context passage in the prompt, and will not yield consistent or positive results if open-context prompting in which you are looking for the model to draw upon potential background knowledge of the world - in fact, it is likely that the BLING will respond with a simple "Not Found." to an open context query.  
-
-Any model can provide inaccurate or incomplete information, and should be used in conjunction with appropriate safeguards and fact-checking mechanisms.
-
-
-## How to Get Started with the Model
-
-To pull the model via API:  
-
-    from huggingface_hub import snapshot_download           
-    snapshot_download("llmware/bling-phi-3-gguf", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)  
-    
-Load in your favorite GGUF inference engine, or try with llmware as follows:
-
-    from llmware.models import ModelCatalog  
-    
-    # to load the model and make a basic inference
-    model = ModelCatalog().load_model("llmware/bling-phi-3-gguf", temperature=0.0, sample=False)
-    response = model.inference(query, add_context=text_sample)  
+- **Finetuned from model:** Mistral-7B-0.3-Base
 
 Details on the prompt wrapper and other configurations are on the config.json file in the files repository.