perpendicularai
diff --git a/‎README.md
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+2-6Lines changed: 2 additions & 6 deletions b/‎README.md
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diff --git a/‎llama_cpp/llama.py
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+3-7Lines changed: 3 additions & 7 deletions b/‎llama_cpp/llama.py
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+3-7Lines changed: 3 additions & 7 deletions
@@ -654,19 +654,15 @@ llm = Llama(model_path="./models/7B/llama-model.gguf", n_ctx=2048)
 To utilise the cross-encoder, you follow the below code instructions:
 ```python
 # Define the location of your nltk data directory
-data_path1 = llama_cpp.nlLoader(nltkData="<PATH TO NLTK DATA DIRECTORY>")
+llmx = LlamaX("<PATH TO YOUR NLTK DATA DIRECTORY>")
+data_path1 = llama_cpp.nlLoader(nltkData=llmx)
 
 # Initialize the sentence-splitter function
 paragraphs1 = llama_cpp.sentSplit()
 
 # Define the passSearch function to join sentences
 passages1 = llama_cpp.passSearch()
 
-# Call the LlamaX class to define the model. Add the path to your Bert cross-encoder.
-import os
-username = os.getenv("USERNAME")
-modelPath = LlamaX()
-
 # Load LlamaCpp GGUF embeddings model and parse in the searchQuery function with the scored passages to generate embeddings
 username = os.getenv("USERNAME")
 print("")
 
@@ -56,6 +56,9 @@
 from ._logger import set_verbose
 from ._utils import suppress_stdout_stderr
 
+class LlamaX(str):
+    pass
+
 username = os.getenv("USERNAME")
 
 def models():
@@ -65,13 +68,6 @@ def models():
     except Exception as e:
         print("Please add your cross-encoder model.")
 
-class LlamaX:
-    def __init__(self):
-        self.model_path = models()
-
-    def get_model_path(self) -> str:
-        return self.model_path
-
 def nlLoader(nltkData):
     nltk_data_dir = Path(nltkData)
     nltk.data.path.append(str(nltk_data_dir))