Update README.md

README.md

@@ -15,16 +15,16 @@ language:
 pipeline_tag: text-generation
 ---
 
-# TBH.AI Vortex Reasoning  
+# Clyrai Vortex Reasoning  
 
-- **Developed by:** TBH.AI 
+- **Developed by:** clyrai 
 - **License:** apache-2.0  
 - **Fine-tuned from:** [saishshinde15/TBH.AI_Base_Reasoning](https://huggingface.co/saishshinde15/TBH.AI_Base_Reasoning)  
 - **Category:** Experimental, Research  
 
 ## **Introduction**  
 
-TethysAI Vortex Reasoning is an **experimental model** that advances the structured reasoning capabilities pioneered by [TBH.AI Base Reasoning](https://huggingface.co/saishshinde15/TethysAI_Base_Reasoning). While the Base Reasoning model utilized **Generalized Reinforced Policy Optimization (GRPO)** to enhance step-by-step logical thought processes similar to **DeepSeek-R1**, this model takes a different approach—**eliminating GRPO and instead relying on high-end Supervised Fine-Tuning (SFT) techniques**.  
+TethysAI Vortex Reasoning is an **experimental model** that advances the structured reasoning capabilities pioneered by [Clyrai Base Reasoning](https://huggingface.co/saishshinde15/TethysAI_Base_Reasoning). While the Base Reasoning model utilized **Generalized Reinforced Policy Optimization (GRPO)** to enhance step-by-step logical thought processes similar to **DeepSeek-R1**, this model takes a different approach—**eliminating GRPO and instead relying on high-end Supervised Fine-Tuning (SFT) techniques**.  
 
 The core objective was to investigate whether **deep reasoning and self-questioning behavior could emerge purely through SFT on high-quality datasets**. The results were highly promising: the model successfully **questions itself internally**, improves reasoning depth, and consistently generates structured, logical responses.  
 
@@ -49,7 +49,7 @@ To compensate for the lack of reinforcement learning, we used an **extensive dat
 The model internally uses **special reasoning markers** (`<think>` and `<answer>`) to structure its responses, though these may not always be visible in the final output. This ensures a **consistent and methodical approach** to answering questions.  
 
 ### **5️⃣ Part of the TethysAI Vortex Family**  
-This model belongs to the **TBH.AI Vortex series**, a collection of fine-tuned models pushing the boundaries of **SFT-based reasoning without reinforcement learning**.  
+This model belongs to the **Clyrai Vortex series**, a collection of fine-tuned models pushing the boundaries of **SFT-based reasoning without reinforcement learning**.  
 
 ---
 
@@ -76,7 +76,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
 # Load model & tokenizer
-model_name = "saishshinde15/TBH.AI_Vortex_Reasoning"
+model_name = "saishshinde15/Clyrai_Vortex_Reasoning"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda")