Update README.md

README.md

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 license: cc-by-4.0
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-# Mermaid-Llama-2-13B
+# Mermaid-Llama-6.7B
 
-Introducing Mermaid-Llama-13B-Detailed-Responses, a robust language model designed to master Python intricacies and craft captivating story flow maps in more depth and detail than previous models. 
-With a staggering 13 billion parameters, this model sets the bar for excellence in AI-driven code comprehension and narrative visualization.
+Introducing Mermaid-Llama-6.7B-Detailed-Responses, a robust language model designed to master Python intricacies and craft captivating story flow maps in more depth and detail than previous models. 
+With a staggering 6.7 billion parameters, this model sets the bar for excellence in AI-driven code comprehension and narrative visualization.
 
 ![MermaidLlama GIF](Mermaid_ShowCase/MermaidLlama.webp)
 
@@ -32,7 +32,7 @@ With a staggering 13 billion parameters, this model sets the bar for excellence
 Interested in enhancing Mermaid's capabilities? Contact troydoesai@gmail.com for collaboration opportunities.
 
 ## Example Use Cases
-- **Retrieval-Augmented Generation (RAG):** Utilize Mermaid-LLama-3-8B to create condensed knowledge graphs. This model excels in generating flow diagrams that enhance the retrieval process. These knowledge graphs are stored in a vector database, which allows for quick and efficient retrieval of contextually relevant information. When a query is received, the system retrieves a pertinent knowledge graph, appending it as context to the model. This enriched context enables Mermaid-LLama-3-8B to deliver more accurate and nuanced responses. This approach is particularly beneficial in applications requiring deep, context-aware interactions, such as sophisticated Q&A systems, dynamic data analysis, and complex decision-making tasks.
+- **Retrieval-Augmented Generation (RAG):** Utilize Mermaid-LLama to create condensed knowledge graphs. This model excels in generating flow diagrams that enhance the retrieval process. These knowledge graphs are stored in a vector database, which allows for quick and efficient retrieval of contextually relevant information. When a query is received, the system retrieves a pertinent knowledge graph, appending it as context to the model. This enriched context enables Mermaid-LLama-3-8B to deliver more accurate and nuanced responses. This approach is particularly beneficial in applications requiring deep, context-aware interactions, such as sophisticated Q&A systems, dynamic data analysis, and complex decision-making tasks.
 - **Code Documentation:** Automatic visual flow charts from Python code.
 - **Storyboarding:** Visually appealing diagrams for storytelling.
 - **Project Planning:** Visual project flow maps for effective team communication.