Update README.md

README.md

@@ -13,12 +13,12 @@ base_model:
 
 # Nakshatra: Human-like Conversational AI Prototype
 
-![logo](https://huggingface.co/Abhaykoul/Nakshatra/resolve/main/Designer.png)
+![logo](https://huggingface.co/OEvortex/Nakshatra/resolve/main/Designer.png)
 
 ## Overview  
 Nakshatra is a groundbreaking prototype AI model, boasting **10x** better human-like responses compared to the previous HelpingAI models. Designed by **Abhay Koul (OEvortex)**, Nakshatra leverages advanced conversational techniques to deliver highly coherent, empathetic, and contextually aware interactions, making it a major leap forward in AI-human interaction.
 
-- Delivers near-human conversational quality and responsiveness.
+- Delivers near-human conversational quality and responsiveness.- Delivers near-human conversational quality and responsiveness.
 - Exhibits deep contextual understanding and emotional intelligence in interactions.
 - Aimed at providing more natural, emotionally intuitive dialogue experiences.- Aimed at providing more natural, emotionally intuitive dialogue experiences.
 
@@ -34,9 +34,9 @@ import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 # Load the Nakshatra model  
-model = AutoModelForCausalLM.from_pretrained("Abhaykoul/Nakshatra", trust_remote_code=True)  
+model = AutoModelForCausalLM.from_pretrained("OEvortex/Nakshatra", trust_remote_code=True)  
 # Load the tokenizer  
-tokenizer = AutoTokenizer.from_pretrained("Abhaykoul/Nakshatra", trust_remote_code=True)  
+tokenizer = AutoTokenizer.from_pretrained("OEvortex/Nakshatra", trust_remote_code=True)  
 
 # Define the chat input  
 chat = [  
@@ -75,9 +75,9 @@ from webscout.Local import formats
 from webscout.Local.samplers import SamplerSettings  
 
 # Download the model  
-repo_id = "Abhaykoul/Nakshatra"  
-filename = "nakshatra-q5_0.gguf"  
-model_path = download_model(repo_id, filename, token="")  
+repo_id = "OEvortex/Nakshatra"  
+filename = "nakshatra-q4_k_m.gguf"  
+model_path = download_model(repo_id, filename, token=None)  
 
 # Load the model  
 model = Model(model_path, n_gpu_layers=40)