Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
 base_model:
-- saishshinde15/TBH.AI_Base_Reasoning
+- saishshinde15/Clyrai_Base_Reasoning
 tags:
 - vortex-family
 - sft
@@ -14,19 +14,19 @@ language:
 - en
 ---
 
-# TBH.AI Vortex  
+# Clyrai Vortex  
 
-- **Developed by:** TBH.AI  
+- **Developed by:** clyrai 
 - **License:** apache-2.0  
-- **Fine-tuned from:** saishshinde15/TBH.AI_Base_Reasoning  
+- **Fine-tuned from:** saishshinde15/Clyrai_Base_Reasoning  
 - **Part of:** Vortex Family (A collection of four fine-tuned SFT models)  
 
 ## **Model Description**  
-TBH.AI Vortex is a **highly refined reasoning model** built upon `saishshinde15/TBH.AI_Base_Reasoning`, further enhanced with **high-quality, curated datasets** that the base model lacked. This model is part of the **Vortex Family**, a series of four fine-tuned models designed for advanced reasoning, knowledge synthesis, and structured response generation.  
+Clyrai Vortex is a **highly refined reasoning model** built upon `saishshinde15/Clyrai_Base_Reasoning`, further enhanced with **high-quality, curated datasets** that the base model lacked. This model is part of the **Vortex Family**, a series of four fine-tuned models designed for advanced reasoning, knowledge synthesis, and structured response generation.  
 
 Unlike typical reinforcement learning-based improvements, **Supervised Fine-Tuning (SFT) was chosen** to ensure greater **control, stability, and alignment with human-preferred responses**, making Vortex more **reliable, interpretable, and useful** across a wide range of tasks.  
 
-## **Why TBH.AI Vortex Stands Out**  
+## **Why Clyrai Vortex Stands Out**  
 - **Enhanced Knowledge & Reasoning**: Incorporates **higher-quality training data** to fill gaps in the base model, improving factual accuracy and logical reasoning.  
 - **Better Response Coherence**: Fine-tuned to provide **more structured, well-reasoned, and contextually relevant answers** across different domains.  
 - **Improved Handling of Complex Queries**: Excels in **multi-step logical deductions, research-oriented tasks, and structured decision-making**.  
@@ -54,7 +54,7 @@ max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
 dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
 load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
 model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name = "saishshinde15/TBH.AI_Vortex",
+    model_name = "saishshinde15/Clyrai_Vortex",
     max_seq_length = max_seq_length,
     dtype = dtype,
     load_in_4bit = load_in_4bit
@@ -96,7 +96,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
 # Load tokenizer and model
-model_name = "saishshinde15/TBH.AI_Vortex"
+model_name = "saishshinde15/Clyrai_Vortex"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)