Update README.md

README.md

@@ -12,14 +12,14 @@ inference: false
     `{'sentiment': ['positive'], people': ['..'], 'organization': ['..'],`  
      `'place': ['..]}`
 
-This 'combo' model is designed to illustrate the potential power of using function calls on small, specialized models to enable a single model architecture to combine the capabilities of what were traditionally two separate model architectures on an encoder.  
+This 3B parameter 'combo' model is designed to illustrate the potential power of using function calls on small, specialized models to enable a single model architecture to combine the capabilities of what were traditionally two separate model architectures on an encoder.  
 
 The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs, providing an intuitive, flexible natural language response, without complex prompting, and with improved generalization and ability to fine-tune to a specific domain use case.  
 
 
 This model is fine-tuned on top of [**llmware/bling-stable-lm-3b-4e1t-v0**](https://huggingface.co/llmware/bling-stable-lm-3b-4e1t-v0), which in turn, is a fine-tune of stabilityai/stablelm-3b-4elt.
 
-For fast inference, we would recommend the 'quantized tool' version of this model, e.g.,  [**'slim-sa-ner-3b-tool'**](https://huggingface.co/llmware/slim-sa-ner-3b-tool).  
+For fast inference, we would recommend the 'quantized tool' version of this model, e.g.,  [**'slim-sa-ner-tool'**](https://huggingface.co/llmware/slim-sa-ner-tool).  
 
 
 ## Prompt format:
@@ -33,8 +33,8 @@ For fast inference, we would recommend the 'quantized tool' version of this mode
 <details>
 <summary>Transformers Script </summary>
 
-    model = AutoModelForCausalLM.from_pretrained("llmware/slim-sa-ner-3b")
-    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sa-ner-3b")
+    model = AutoModelForCausalLM.from_pretrained("llmware/slim-sa-ner")
+    tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sa-ner")
 
     function = "classify"
     params = "topic"