Update README.md

README.md

@@ -2,26 +2,22 @@
 license: cc-by-sa-4.0
 ---
 
-# SLIM-SA-NER-3B-TOOL
+# SLIM-BOOLEAN-TOOL
 
 <!-- Provide a quick summary of what the model is/does. -->
 
 
-**slim-sa-ner-3b-tool** is a 4_K_M quantized GGUF version of slim-sa-ner-3b, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.  
+**slim-boolean-tool** is a 4_K_M quantized GGUF version of slim-boolean, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.  
 
-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.  
+This is an experimental model that takes as input a context passage, a yes-no question, and an optional (explain) parameter, and generates a response consisting of a python dictionary with two keys- 'answer' consisting of the 'yes/no' classification, and 'explanation' which provides a text explanation, derived from the source passage that explains the boolean classification assesment.  All of the details on the prompt template as provided in the config.json file in this model repo, along with several examples.  
 
-The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs.
 
-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.
+[**slim-boolean**](https://huggingface.co/llmware/slim-boolean) 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.
 
 To pull the model via API:  
 
     from huggingface_hub import snapshot_download           
-    snapshot_download("llmware/slim-sa-ner-3b-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)  
+    snapshot_download("llmware/slim-boolean-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)  
     
 
 Load in your favorite GGUF inference engine, or try with llmware as follows:
@@ -29,14 +25,14 @@ Load in your favorite GGUF inference engine, or try with llmware as follows:
     from llmware.models import ModelCatalog  
     
     # to load the model and make a basic inference
-    model = ModelCatalog().load_model("slim-sa-ner-3b-tool")
+    model = ModelCatalog().load_model("slim-boolean-tool")
     response = model.function_call(text_sample)  
 
     # this one line will download the model and run a series of tests
-    ModelCatalog().tool_test_run("slim-sa-ner-3b-tool", verbose=True)  
+    ModelCatalog().tool_test_run("slim-boolean-tool", verbose=True)  
 
 
-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.  
+Note: please review [**config.json**](https://huggingface.co/llmware/slim-boolean-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.  
 
 
 ## Model Card Contact