Update README.md

README.md

@@ -11,9 +11,9 @@ license: cc-by-sa-4.0
 
 This model combines two of the most popular traditional classifier capabilities (**sentiment analysis** and **named entity recognition**) and re-images them as function calls on a small specialized decoder LLM, generating output in the form of a python dictionary with keys corresponding to sentiment and NER identifiers.  
 
-The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs:  
+The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs.
 
-The size of self-contained model binary is 1.71 GB, which is small enough to run locally on a CPU, and yet which comparables favorably with the use of two traditional FP32 versions of Roberta-Large for NER (1.42GB) and BERT for Sentiment Analysis (440 MB), while offering greater potential capacity depth with 2.7B parameters, and without the requirement of Pytorch and other external dependencies. 
+The size of the self-contained GGUF model binary is 1.71 GB, which is small enough to run locally on a CPU, and yet which comparables favorably with the use of two traditional FP32 versions of Roberta-Large for NER (1.42GB) and BERT for Sentiment Analysis (440 MB), while offering greater potential capacity depth with 2.7B parameters, and without the requirement of Pytorch and other external dependencies. 
 
 
 [**slim-sa-ner-3b**](https://huggingface.co/llmware/slim-sa-ner-3b) is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling.
@@ -36,15 +36,6 @@ Load in your favorite GGUF inference engine, or try with llmware as follows:
     ModelCatalog().tool_test_run("slim-sa-ner-3b-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("sa-ner")
-    response = llm_fx.sa_ner(text)  
-
-
 Note: please review [**config.json**](https://huggingface.co/llmware/slim-sa-ner-3b-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.