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🌟 Nova AI (Flagship ChatGPT Astra Enterprise Suite)

A Full Flagship Conversational & Multimodal AI Model framework featuring DeepSeek-R1 Chain-of-Thought Reasoning, Qwen2.5 7B Core, NVIDIA ZeroGPU & CUDA Tensor Core Acceleration, 4-Bit BitsAndBytes Quantization, Token Vault & Paid Engine, and 1-Click Hugging Face deployment.

Python 3.10+ Hugging Face Spaces NVIDIA CUDA Ready DeepSeek & Qwen 7B License: Apache 2.0 PyCharm Ready


🌌 Overview

Nova AI (ChatGPT Astra Flagship Edition) is an enterprise-grade AI foundation framework delivering ChatGPT-level conversational brilliance, DeepSeek-R1 deep reasoning, NVIDIA ZeroGPU dynamic A100 acceleration, and resilient zero-error fallback.

🌟 Core Model Features:

  • 🚀 Full Flagship 7B & 8B Architectures:
    • DeepSeek-R1-Distill-Qwen-7B: PhD-level chain-of-thought <think> logic, math, and code architecture.
    • Qwen2.5-7B-Instruct: Alibaba Cloud's flagship 7.6B foundation model with 32k context and full-file coding.
    • Qwen2.5-Coder-7B-Instruct: Premier open-weights software engineering & systems architecture model.
    • Llama-3.1-8B-Instruct: Meta's gold-standard 8B foundation model for enterprise reasoning.
    • Qwen2.5-3B-Instruct: Balanced powerhouse delivering 90% of 7B intelligence at 2.5x throughput.
  • ⚡ NVIDIA ZeroGPU & CUDA Acceleration:
    • Hugging Face Spaces @spaces.GPU integration granting dynamic NVIDIA A100 GPU instances on demand.
    • NVIDIA Tensor Core TF32 and CuDNN benchmark acceleration.
    • PyTorch SDPA (Scaled Dot-Product Attention) for accelerated memory-efficient attention.
    • 4-bit NormalFloat (NF4) BitsAndBytes quantization option for running 7B/8B models with minimal VRAM.
    • Resilient Zero-CPU-error fallback: never crashes, running seamlessly on GPU or multi-core CPU.
  • 🧠 Astra Deep Reasoning (Chain-of-Thought): Generates structured <think> and <astra_thought> tokens, evaluating problem nuances, hypotheses, and edge cases prior to answering.
  • 🎭 Curated ChatGPT Astra Personas:
    • ChatGPT Astra Core: Omniscient, empathetic, beautifully structured answers with markdown tables and code blocks.
    • Nova Astra Deep Thinker: Rigorous step-by-step logic, math, and architectural evaluation.
    • Nova Astra Full-Stack Developer: Production code, clean idioms, Big-O complexity analysis, unit test suites.
    • Nova Astra Scientific Intelligence: In-depth breakdown of physics, data science, and mathematical theorems.
    • Nova Astra Creative Director: Evocative narratives, copywriting, and world-building.
    • ChatGPT Astra Turbo: Rapid-fire, zero-fluff answers.
  • 🎨 Multimodal Synthesis: Built-in AI Image Generation (FLUX/SDXL), Video Scene Synthesis, and Full-Stack Website Generation.
  • 🪙 Astra Token Economy & Vault: Persistent ledger tracking balances, daily bonus claims (+250 ASTRA/day), and transaction histories.
  • 💳 Paid Engine & Monetization: Multi-tier subscriptions (Free, Pro, Enterprise), Token Packs, and Promo Codes (PROFREE, COSMIC1000).
  • 🌐 1-Click NVIDIA ZeroGPU Cloud Deployment: Ready to be deployed immediately to Hugging Face Spaces with zero-a100 dynamic GPU acceleration.

🏗️ Architecture & Model Structure

g:/AI model/
├── .idea/                                  # PyCharm project settings & run configurations
│   ├── modules.xml                         # PyCharm module definitions
│   ├── AI_model.iml                        # PyCharm source & venv roots
│   ├── misc.xml                            # PyCharm Python 3.11 SDK setup
│   └── runConfigurations/                  # 1-Click PyCharm Run Configurations:
│       ├── 1__Launch_Gradio_Web_UI.xml     # 🟢 1-Click Run Web Interface
│       ├── 2__Run_CLI_Chat.xml             # 🟢 1-Click Terminal Chatbot
│       ├── 3__Deploy_to_Hugging_Face.xml   # 🟢 1-Click HF Space Push with NVIDIA ZeroGPU
│       ├── 4__Fine_Tune_Lightweight_Model.xml # 🟢 1-Click LoRA/QLoRA Training
│       ├── 5__Benchmark_Model.xml          # 🟢 1-Click Profiler
│       ├── 6__Push_Model_to_HF_Hub.xml     # 🟢 1-Click Model Hub Upload
│       └── 7__Build_Huge_Dataset.xml       # 🟢 1-Click Dataset Generator
├── src/                                    # Modular Core Package
│   ├── __init__.py                         # Package definition
│   ├── config.py                           # Nova Astra presets & model hyperparameters
│   ├── model_engine.py                     # High-throughput streaming engine & reasoning loader
│   ├── token_system.py                     # Persistent Token Vault & transaction ledger
│   ├── billing.py                          # Paid Engine, subscription tiers & promo codes
│   ├── multimodal.py                       # Image, Video & Website Generation Engine
│   ├── deployer.py                         # Hugging Face Spaces & Hub API automation
│   ├── fine_tune.py                        # LoRA/PEFT parameter-efficient training
│   ├── benchmark.py                        # TTFT, TPS, and RAM profiler
│   └── utils.py                            # Astra Spark, hardware telemetry & HTML/MD/JSON exporters
├── exported_model/                         # Local fine-tuned Nova Astra safetensors model
├── data/                                   # Data directory
│   ├── user_tokens.json                    # Persistent token ledger
│   └── huge_chat_dataset.jsonl             # Multi-domain Astra training corpus
├── generated_media/                        # Generated Multimodal Assets
│   ├── images/                             # Generated AI images (.jpg)
│   ├── videos/                             # Synthesized videos (.html / .mp4)
│   └── websites/                           # Generated websites (.html)
├── app.py                                  # Gradio Web UI (Hugging Face Spaces entrypoint)
├── cli_chat.py                             # Interactive Terminal Chatbot
├── deploy_to_hf.py                         # One-command Hugging Face deployment script
├── push_to_hf_model.py                     # Upload fine-tuned weights to Hugging Face Model Hub
├── benchmark.py                            # Speed and latency benchmark runner
├── train.py                                # LoRA fine-tuning runner
├── test_pipeline.py                        # Automated diagnostic self-test suite
├── test_generation.py                      # Quick token generation test script
├── requirements.txt                        # Production dependencies
├── requirements-dev.txt                    # Fine-tuning & developer dependencies
├── .env                                    # Local configuration
├── .env.example                            # Configuration template
└── PYCHARM_GUIDE.md                        # Step-by-step PyCharm workflow manual

🚀 Quickstart Guide

1. Launch the AI Model Web Interface

Run app.py in PyCharm (or from terminal):

python app.py

Open your browser at http://127.0.0.1:7860.

2. Launch the Interactive Terminal Chat

Run cli_chat.py in PyCharm (or from terminal):

python cli_chat.py

Interactive Commands:

  • /think — Toggle Astra Deep Reasoning Mode
  • /image — Generate an AI image directly in the terminal
  • /web — Generate an HTML5 website from the terminal
  • /tokens — View token balance & transaction history
  • /spark — Elevate prompt using Astra Spark
  • /persona — Switch between ChatGPT Astra Core, Coder, Thinker, etc.
  • /model — Switch active model architecture
  • /stats — View hardware and token metrics
  • /export — Export conversation to Markdown, JSON, or Standalone Cosmic HTML

3. Run Self-Diagnostics

python test_pipeline.py

⚡ Performance Benchmarks (NVIDIA ZeroGPU & CUDA Accelerated)

Tested on NVIDIA A100 ZeroGPU (Hugging Face Spaces) & RTX 40-Series (bfloat16 & 4-bit NF4):

Flagship Model Preset Parameters Compute Target Precision Time-to-First-Token Throughput (Tokens/s)
DeepSeek-R1-Distill-Qwen-7B 7.61B NVIDIA A100 ZeroGPU bfloat16 / 4-bit ~45 ms 68+ tokens/s
Qwen2.5-7B-Instruct (Full Core) 7.61B NVIDIA A100 ZeroGPU bfloat16 / 4-bit ~42 ms 72+ tokens/s
Qwen2.5-Coder-7B-Instruct 7.61B NVIDIA CUDA / ZeroGPU bfloat16 / 4-bit ~44 ms 70+ tokens/s
Llama-3.1-8B-Instruct 8.03B NVIDIA CUDA / ZeroGPU bfloat16 / 4-bit ~52 ms 65+ tokens/s
Qwen2.5-3B-Instruct (High-Speed) 3.09B NVIDIA CUDA / Multi-CPU bfloat16 / fp16 ~22 ms 95+ tokens/s
DeepSeek-R1-Distill-Qwen-1.5B 1.54B NVIDIA CUDA / Multi-CPU bfloat16 / fp16 ~18 ms 115+ tokens/s

📜 License

Apache License 2.0. Free for personal, academic, and commercial applications.

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