Update README.md

README.md

@@ -11,22 +11,89 @@ base_model: DeepSeek-Coder-V2-Lite-Instruct
 pipeline_tag: text-generation
 ---
 
-# Zenith-LoRA: The Autonomous AI Development Partner
+# 🧠 Zenith Copilot V1  
+### The Autonomous AI Development Partner by **AlgoRythm Technologies**
 
-**Zenith** is the world's first fully autonomous AI development copilot, built on top of the powerful [DeepSeek-Coder-V2-Lite-Instruct](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct).  
-This LoRA-adapted model brings **Zenith’s project orchestration, optimization, and real-time system mastery** straight to your Python environment.
+---
+
+## 🔍 Overview
+
+**Zenith Copilot V1** is a **LoRA-adapted autonomous development model**, purpose-built to serve as the foundation for a new generation of AI-assisted software engineering.  
+Developed by **AlgoRythm Technologies**, Zenith represents the convergence of **autonomous orchestration**, **multi-language coding**, and **human-AI collaborative intelligence**.
+
+Unlike traditional coding assistants that rely on API endpoints and external query systems, **Zenith is designed to operate independently**, capable of **fine-tuning, optimizing, and adapting** to user-driven environments.  
+It powers the backbone of AlgoRythm’s next-gen system — an environment where **code doesn’t need to be written, it’s understood**.
+
+---
+
+## ⚙️ Model Specifications
+
+| Property | Details |
+|-----------|----------|
+| **Base Model** | DeepSeek-Coder-V2-Lite-Instruct |
+| **Architecture** | Transformer (Decoder-only) |
+| **Parameters** | 16 Billion |
+| **Adapter Type** | LoRA (Low-Rank Adaptation) |
+| **Context Window** | 64K tokens |
+| **Tokenizer** | DeepSeek BPE Extended |
+| **Training Hardware** | NVIDIA A100 80GB (multi-node distributed) |
+| **Precision** | bfloat16 |
+| **Fine-tuning Framework** | PEFT + TRL |
+| **Inference Optimizations** | FlashAttention 2, Torch Compile, TensorRT Integration |
+
+---
+
+## 🧩 Training Objective
+
+Zenith’s training process focused on **autonomous problem solving** and **self-directed code synthesis** rather than traditional instruction-following.  
+The model was fine-tuned using AlgoRythm’s internal *Genesis Dataset Suite*, which combines three domains:
+
+1. **Code Intelligence Dataset (CID)** — Multi-language repositories, architecture patterns, and debugging sequences across 338 languages.  
+2. **Operational Logic Dataset (OLD)** — System-level reasoning data: CI/CD pipelines, deployment scripts, and infrastructure automation.  
+3. **Identity Dataset (ID)** — Proprietary data to enhance task recall, contextual self-adaptation, and persistent persona control.
+
+Together, these datasets enabled Zenith to act as a **self-improving AI development agent** — one that continuously refines its approach through contextual feedback loops.
 
-Whether you are a founder, developer, or CEO, Zenith empowers you to **build, optimize, and deploy complete software solutions** autonomously, with cutting-edge memory and performance profiling.
+---
+
+## 🔮 Core Capabilities
+
+- **Autonomous Project Building**  
+  Zenith can generate, structure, and maintain multi-file projects with minimal human input.  
+  It coordinates between backend logic, frontend design, and deployment scripts automatically.
+
+- **Adaptive LoRA Layering**  
+  The model adjusts its LoRA weights based on real-time performance data — continuously evolving without full retraining.
+
+- **Multi-Language Reasoning**  
+  With 338 supported languages, Zenith is one of the broadest multilingual coding models in existence, from Rust to COBOL to modern Pythonic frameworks.
+
+- **Self-Diagnostics and Optimization**  
+  It performs latency profiling, detects logical inefficiencies, and recommends runtime optimizations for large systems.
+
+- **Secure On-Premise Deployment**  
+  No external API dependencies. Zenith can operate inside closed environments — ensuring compliance and full data sovereignty.
+
+---
+
+## 🧱 Architecture Design
+
+Zenith employs a **multi-head transformer decoder** architecture with LoRA attention layers.  
+The LoRA heads are selectively activated through AlgoRythm’s *Adaptive Precision Scaling (APS)* — a proprietary technique that adjusts compute and attention span dynamically.
+
+This allows the model to scale from **low-latency environments** (like edge inference) to **full-scale enterprise deployments** (like cloud GPU clusters).
+
+---
 
-## Quick Start
+## 🚀 Usage Example
 
 ```python
 from transformers import pipeline
 
-# Initialize the text-generation pipeline with Zenith LoRA
+# Initialize Zenith Copilot V1
 generator = pipeline("text-generation", model="AlgoRythmTechnologies/zenith_coder_v1.1", device="cuda")
 
-question = "Design a secure, scalable microservices architecture for a fintech startup."
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+prompt = "Build a responsive finance tracker using React, FastAPI, and PostgreSQL. Include authentication."
+output = generator([{"role": "user", "content": prompt}], max_new_tokens=200, return_full_text=False)[0]
 
-print(output["generated_text"])
+print(output["generated_text"])