Upload README.md

README.md

@@ -6,24 +6,29 @@ license: apache-2.0
 
 <!-- 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-ner-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.
+slim-ner-tool is a 4_K_M quantized GGUF version of slim-ner, providing a small, fast inference implementation.
 
-Load in your favorite GGUF inference engine, or try with llmware as follows:
+Load in your favorite GGUF inference engine (see details in config.json to set up the prompt template), 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")
+    # to load the model and make a basic inference
+    ner_tool = ModelCatalog().load_model("slim-ner-tool")
+    response = ner_tool.function_call(text_sample)  
 
-Slim models can also be loaded even more simply as part of LLMfx calls:
+    # this one line will download the model and run a series of tests
+    ModelCatalog().test_run("slim-ner-tool", verbose=True)  
+    
+    
+Slim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls:
 
     from llmware.agents import LLMfx
 
     llm_fx = LLMfx()
-    llm_fx.load_tool("sentiment")
-    response = llm_fx.sentiment(text)
+    llm_fx.load_tool("ner")
+    response = llm_fx.named_entity_extraction(text)
 
 
 ### Model Description
@@ -40,19 +45,10 @@ Slim models can also be loaded even more simply as part of LLMfx calls:
 
 <!-- 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.  
+SLIM models provide a fast, flexible, intuitive way to integrate classifiers and structured function calls into RAG and LLM application workflows.  
 
-Example:
+Model instructions, details and test samples have been packaged into the config.json file in the repository, along with the GGUF file.   
 
-    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:
-
-    "<human> " + text + "<classify> " + keys + "</classify>" + "/n<bot>: "
 
 
 ## Model Card Contact