Update README.md

README.md

@@ -9,24 +9,17 @@ license: apache-2.0
 pipeline_tag: text-generation
 ---
 
-# XeroAI Conversational Model
-Welcome to the XeroAI Conversational Model repository! This model has been fine-tuned from the unsloth/llama-2-7b-chat-bnb-4bit base on the piotr25691/ultrachat-200k-alpaca dataset to enhance its conversational abilities. It is designed to provide more natural, engaging, and contextually aware responses.
+# Xander Conversational Model
+Welcome to the Xander Conversational Model repository! This model has been fine-tuned from the unsloth/llama-2-7b-chat-bnb-4bit base on the piotr25691/ultrachat-200k-alpaca dataset to enhance its conversational abilities. It is designed to provide more natural, engaging, and contextually aware responses.
 
 # Introduction
-The XeroAI Conversational Model is an advanced NLP model aimed at improving interactive text generation. By leveraging the strengths of unsloth/llama-2-7b-chat-bnb-4bit and fine-tuning it with the extensive piotr25691/ultrachat-200k-alpaca dataset, the model is adept at generating coherent and contextually relevant conversations.
+The Xander Conversational Model is an advanced NLP model aimed at improving interactive text generation. By leveraging the strengths of unsloth/llama-2-7b-chat-bnb-4bit and fine-tuning it with the extensive piotr25691/ultrachat-200k-alpaca dataset, the model is adept at generating coherent and contextually relevant conversations.
 
 # Features
 - Improved Conversational Flow: Generates more natural and engaging responses.
 - Context Awareness: Maintains context over multiple interactions.
-- Efficient: Utilizes a 4-bit quantized model for better performance with reduced computational resources.
 - Customizable: Can be further fine-tuned for specific applications or industries.
 
-# Model Details
-- Base Model: unsloth/llama-2-7b-chat-bnb-4bit
-- Fine-tuning Dataset: piotr25691/ultrachat-200k-alpaca
-- Parameters: 7 billion
-- Quantization: 4-bit, 16-bit, 32-bit
-
 # Dataset
 The model was fine-tuned on the piotr25691/ultrachat-200k-alpaca dataset, which consists of 200,000 high-quality conversational pairs. This dataset helps the model to understand and generate more nuanced and contextually appropriate responses.