Update README.md

README.md

@@ -1,29 +1,19 @@
 ---
-license: apache-2.0
+license: llama2
 ---
 
 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-**slim-sentiment-tool** is part of the SLIM ("Structured Language Instruction Model") model series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling.  
-
-slim-sentiment-tool is a 4_K_M quantized GGUF version of slim-sentiment-tool, providing a fast, small inference implementation.
+**dragon-llama-qa-tool** is a 4_K_M quantized GGUF version of DRAGON Llama, providing a fast, small inference implementation.
 
 Load in your favorite GGUF inference engine, or try with llmware as follows:
 
     from llmware.models import ModelCatalog
 
-    sentiment_tool = ModelCatalog().load_model("llmware/slim-sentiment-tool")
-    response = sentiment_tool.function_call(text_sample, params=["sentiment"], function="classify")
-
-Slim models can also be loaded even more simply as part of LLMfx calls:
-
-    from llmware.agents import LLMfx
-
-    llm_fx = LLMfx()
-    llm_fx.load_tool("sentiment")
-    response = llm_fx.sentiment(text)
+    qa_tool = ModelCatalog().load_model("llmware/dragon-llama-qa-tool")
+    response = qa_tool.inference(query, text_sample)
 
 
 ### Model Description
@@ -34,25 +24,15 @@ Slim models can also be loaded even more simply as part of LLMfx calls:
 - **Model type:** GGUF 
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
-- **Quantized from model:** llmware/slim-sentiment (finetuned tiny llama)
+- **Quantized from model:** llmware/dragon-llama (finetuned llama)
 
 ## 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 SLIM models is to re-imagine traditional 'hard-coded' classifiers through the use of function calls.  
-
-Example:
-
-    text = "The stock market declined yesterday as investors worried increasingly about the slowing economy."   
-
-    model generation - {"sentiment": ["negative"]}
-
-    keys = "sentiment"
-
-All of the SLIM models use a novel prompt instruction structured as follows:
+All of the DRAGON models use the following prompt wrapper template:
 
-    "<human> " + text + "<classify> " + keys + "</classify>" + "/n<bot>: "
+    "<human> " + context + "\n" + question + "\n<bot>: "
 
 
 ## Model Card Contact