Update README.md

README.md

@@ -6,20 +6,18 @@ license: apache-2.0
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-**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-ner-tool is a 4_K_M quantized GGUF version of slim-ner, providing a small, fast inference implementation.
+**bling-qa-tool** is a 4_K_M quantized GGUF version of bling-tiny-llama-1b-v0, providing a small, fast inference implementation.
 
 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
 
     # to load the model and make a basic inference
-    ner_tool = ModelCatalog().load_model("slim-ner-tool")
-    response = ner_tool.function_call(text_sample)  
+    qa_tool = ModelCatalog().load_model("bling-qa-tool")
+    response = qa_tool.function_call(text_sample)  
 
     # this one line will download the model and run a series of tests
-    ModelCatalog().test_run("slim-ner-tool", verbose=True)  
+    ModelCatalog().test_run("bling-qa-tool", verbose=True)  
     
     
 Slim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls:
@@ -27,8 +25,8 @@ Slim models can also be loaded even more simply as part of a multi-model, multi-
     from llmware.agents import LLMfx
 
     llm_fx = LLMfx()
-    llm_fx.load_tool("ner")
-    response = llm_fx.named_entity_extraction(text)
+    llm_fx.load_tool("quick_question")
+    response = llm_fx.quick_question(text)
 
 
 ### Model Description
@@ -39,18 +37,15 @@ Slim models can also be loaded even more simply as part of a multi-model, multi-
 - **Model type:** GGUF 
 - **Language(s) (NLP):** English
 - **License:** Apache 2.0
-- **Quantized from model:** llmware/slim-sentiment (finetuned tiny llama)
+- **Quantized from model:** llmware/bling-tiny-llama-1b-v0 
 
 ## 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. -->
 
-SLIM models provide a fast, flexible, intuitive way to integrate classifiers and structured function calls into RAG and LLM application workflows.  
-
 Model instructions, details and test samples have been packaged into the config.json file in the repository, along with the GGUF file.   
 
 
-
 ## Model Card Contact
 
 Darren Oberst & llmware team