Update README.md

README.md

@@ -16,45 +16,49 @@ library_name: pytorch
 
 # Hexa-1B (Prototype)
 
-**Developed by:** Madhab ([Hexa Innovate Org](https://github.com/Hexa08))  
+**Developed by:** Madhab (Founder, Hexa Innovate Org)  
 **Architecture:** HexaDense (Transformer Decoder)  
 **Format:** [NEF (Neural Essence Format)](https://github.com/Hexa08/NEF)  
 **Status:** Research Prototype (1.1 Billion Parameters)
 
 ---
 
-## 🚀 The Mission
-Hexa-1B is a billion-scale language model built as a proof-of-concept for the **Neural Essence Format (NEF)**. This project demonstrates that state-of-the-art transformer architectures can be engineered, trained, and serialized by a **single developer** using a streamlined, high-performance format that challenges traditional, bloated AI frameworks.
+## Model Summary
+Hexa-1B is a 1.1-billion parameter large language model engineered as a proof-of-concept for the Neural Essence Format (NEF). This project demonstrates the feasibility of building and training billion-scale transformer architectures by a solo developer using an optimized, modular serialization framework.
 
-## 🛠️ Technical Framework: NEF
-Unlike standard `.bin` or `.safetensors` files, this model is built using **NEF (Neural Essence Format)**. 
-* **Efficiency:** Optimized binary serialization for rapid weight loading.
-* **Modularity:** Specifically designed to support the Hexa AI ecosystem.
-* **Portability:** Built for cross-environment execution with minimal dependencies.
+## Technical Framework: NEF
+This model utilizes the Neural Essence Format (NEF) for weight serialization and architectural definition. NEF is designed to provide a high-performance alternative to traditional model formats, focusing on:
+* **Binary Efficiency:** Optimized for rapid loading and minimal overhead.
+* **Modular Logic:** Tailored for seamless integration with custom inference engines.
+* **Streamlined Execution:** Reduced dependency footprint for deployment in resource-constrained environments.
 
-Check out the framework here: [github.com/Hexa08/NEF](https://github.com/Hexa08/NEF)
+Repository: [github.com/Hexa08/NEF](https://github.com/Hexa08/NEF)
 
-## 📊 Model Specifications
-* **Parameter Count:** 1.1 Billion
+## Model Specifications
+* **Parameters:** 1.1 Billion
 * **Hidden Size:** 1536
 * **Layers:** 16
 * **Attention Heads:** 16
 * **Context Window:** 2048 Tokens
-* **Training Hardware:** 2x NVIDIA Tesla T4 (Dual GPU DataParallel)
+* **Training Hardware:** 2x NVIDIA Tesla T4
+* **Precision:** FP16 (Half Precision)
 
-## 🧠 Solo Developer Narrative
-Hexa-1B is the result of an intensive solo engineering effort in **Cox's Bazar, Bangladesh**. From the ground-up architectural design in PyTorch to the development of the NEF serialization format and the 18-hour training execution on dual T4s, every step was handled by a single founder.
+## Solo Developer Milestone
+The development of Hexa-1B and the NEF framework was conducted entirely by a single engineer based in Cox's Bazar, Bangladesh. The project scope included:
+* Designing the transformer architecture in PyTorch.
+* Developing the NEF binary serialization format.
+* Managing the 18-hour training execution on a dual-GPU cluster.
 
-This model serves as the foundational "intelligence layer" for Hexa Innovate Org, proving that localized, high-capacity AI is achievable without massive corporate research teams.
+This prototype validates that localized, high-capacity AI infrastructure can be established through efficient engineering rather than massive team overhead.
 
-## ⚠️ Research Status & Limitations
-This is a **prototype** version. During training, the model reached a 0.0000 loss state, leading to extreme overfitting (Mode Collapse). 
-* **Current Behavior:** Tends to repeat specific tokens or formats (e.g., "Buildings", "SQLwired").
-* **Recommended Use:** This repository is intended for researchers to inspect the **NEF architecture** and the feasibility of billion-parameter training on mid-range hardware. 
+## Current Limitations and Research Status
+This repository hosts a prototype version of Hexa-1B. During the training phase, the model reached a 0.0000 loss state, resulting in Mode Collapse (extreme overfitting).
+* **Observed Behavior:** The model currently produces repetitive outputs and high-frequency token loops.
+* **Objective:** This release is intended for architectural inspection and to showcase the performance of the NEF framework in handling billion-parameter weights. 
 
 ---
 
-### **About Hexa Innovate Org**
-We are building the next generation of AI infrastructure in Bangladesh, focusing on efficiency, speed, and hardware-agnostic intelligence.
+### About Hexa Innovate Org
+Hexa Innovate Org is dedicated to building efficient, high-speed AI infrastructure in Bangladesh. We focus on localized intelligence and hardware-optimized execution layers.
 
-**Contact:** [Hexa Innovate GitHub](https://github.com/Hexa08)
+**GitHub:** [Hexa08](https://github.com/Hexa08)