Update README.md

README.md

@@ -9,21 +9,11 @@ license: cc-by-sa-4.0
 
 **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.  
 
-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.  
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+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:  
 
-Compared to Encoder Classifiers 
---With 4-bit quantization, this model stand-alone is 1.71 GB binary, which compares favorably with the combination of using FP32 versions of Roberta-Large for NER (1.42GB) and BERT for Sentiment (439 MB), which also require Pytorch and other external dependencies.  
---2.7B parameters vs. Roberta-Large (336M) and BERT (110M)  
---Provides intuitive natural language based responses that fit more naturally in LLM-based agent processes  
---One model can provide multiple modalities of classification, and can be trained using 'union' of two separate datasets  
-
-Compared to Very Large Language Models  
---Runs locally with no API charges
---Does not require complex prompt instructions  
---Small & Specialized - more limited capability but trained to do only 1-2 things well  
+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. 
 
 
 [**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.