🧠 NeuralAI: The Generative AI Engine

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The High-Velocity Model for Your Entire Vibe Stack

NeuralAI is the central intelligence engine developed by De'Andrew Preston Harris. Conceived and engineered by De'Andrew Preston Harris (Founder), it is a highly tuned, DPO-aligned multimodal AI ec[...]

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🌟 Vision & Manifesto

NeuralAI doesn't just predict text; it operates the work. The core mission is to create a multimodal generative system that bridges the gap between raw idea and execution. By fusing autoregressi[...]

Born from resilience and ambition in Memphis, Tennessee and West Memphis, Arkansas, NeuralAI represents a forward-thinking approach to personal, private AI computing.

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🛠️ Tech Stack & Architecture (v7.2)

NeuralAI is built on a high-performance architecture that decouples the inference engine from the web interface, enabling lightweight cloud hosting with powerful local inference.

Core Stack

• Core Model: SmolLM2-360M-Instruct fine-tuned with the custom SFT v16 + DPO v16 LoRA at checkpoints/v2_model — aligned for logic, math, multi-step reasoning, and debugging
• Inference Engine: llmster (LM Studio headless) — OpenAI-compatible API with continuous batching, running via llama.cpp
• Vocal Identity: Andrew (Warm/Multilingual) — Optimized for Live Speech-to-Speech (S2S)
• Backend Framework: Python / Flask (Core Service) — routes to llmster or local PyTorch
• Storage & Database: SQLite3 (Metadata) + Nextcloud Hub via NeuralCloud WebDAV Client (NeuralDrive)
• Frontend UI: Vanilla JS, HTML5, CSS3 with an advanced Dark Mode layout

Pluggable LLM Backend

NeuralAI supports multiple inference backends via the LLM_BACKEND environment variable:

Backend	LLM_BACKEND	API Endpoint	Use Case
llmster (recommended)	lmstudio	http://localhost:1234/v1	Headless GPU/CPU inference
Ollama	ollama	http://localhost:11434/v1	Local Ollama server
OpenAI-compatible	openai_compatible	Any OpenAI API URL	Remote/cloud inference
Local PyTorch	local	Built-in transformers	Loads BASE_MODEL + LoRA at MODEL_PATH in float16 (your own model)
ZO Native (fallback)	zo	https://api.zo.computer/zo/ask	Routes to Zo's own assistant (HY3) — NOT your NeuralAI model; last-resort only

Hosting on ZO Computer (4 GB RAM): set LLM_BACKEND=local. The service loads BASE_MODEL (default HuggingFaceTB/SmolLM2-360M-Instruct) and applies the LoRA at MODEL_PATH (default checkpoints/v2_model) in float16 (~720 MB), which fits the 4 GB host. Do not use LLM_BACKEND=zo for the chat UI — it proxies to Zo's assistant and answers as "Zo Computer's assistant" instead of your trained model.

# Example: start NeuralAI with llmster backend
LLM_BACKEND=lmstudio LLM_API_URL=http://localhost:1234/v1 LLM_MODEL=smollm2 \
  python3 services/neural_core_service.py

Core Architectural Pillars

1. NeuralAI Core: Handles chat state, direct model inference, terminal session proxying, and tool orchestration.
2. NeuralDrive (Cloud Storage): The intelligent data layer for all projects, featuring isolated user storage, automatic versioning, and semantic mapping.
3. Diffusion Engine: An integrated generative diffusion layer for producing visual branding assets, UI mockups, and visual logic maps.
4. Agentic Orchestrator: A high-autonomy layer enabling NeuralAI to plan, reason, and execute multi-step workflows across the OS and web, moving beyond simple chat to active goal achievement.

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🆕 What's New (v7.3.1)

• Developer / API Access (BYO API): Generate a personal API key from Settings to use NeuralAI as an OpenAI-compatible backend on other hosts (e.g. ZO Computer's "Bring Your Own API"). Exposes /v1/chat/completions and /v1/models; keys are hashed and revocable.
• Auto Release Notes ("What's New"): A new top-bar panel surfaces the latest features and fixes automatically. Open it anytime via the ✨ What's New button.
• Generated images render in chat: Image-generation responses are parsed as Markdown and displayed inline.
• No more self-talk: Chat now uses the ChatML prompt template (apply_chat_template), matching the model's training format.
• NeuralDrive upload reliability: The file list correctly handles API response shapes after an upload.
• Dark theme by default: UI restores your saved theme and defaults to dark mode (fixes white file-cards in light mode).
• Phase 8 in progress: Knowledge Graph & Agentic Autonomy — long-term cross-project memory ("Supermemory") and fully autonomous task execution.
• The NeuralLabs Shift: NeuralAI is evolving into a standalone, downloadable intelligence environment (NeuralLabs v1 Client → v2 Edge → v3 Eco).
• v7.3.2 — Backend identity fix (ZO hosting): The hosted service now runs LLM_BACKEND=local so chat uses your SmolLM2-360M + SFT/DPO v16 LoRA. The previous zo fallback proxied to Zo's assistant and answered as "I'm Zo Computer's assistant" — that was a routing bug, not your model. See docs/INCIDENT-2026-07-14-NEURALAI-PAUSES.md.

🚀 Deployment & Model Distribution

• Source (GitHub): Subject-Emu-5259/NeuralAI
• Model (Hugging Face): Subject-Emu-5259/NeuralAI — merged SmolLM2-360M + SFT v16/DPO v16 LoRA weights (drop-in, no PEFT needed). The LoRA adapter is also in checkpoints/v2_model.
• Hosted demo: neuralai-web-ui-deandrewharris.zocomputer.io (ZO Computer) — runs the local backend so chat uses the trained model directly.

To publish the LoRA to Hugging Face:

pip install huggingface_hub
HF_TOKEN=<your-write-token> python3 -c "
from huggingface_hub import HfApi
api = HfApi()
api.upload_folder(
    folder_path='checkpoints/v2_model',
    repo_id='Subject-Emu-5259/NeuralAI',
    repo_type='model',
    commit_message='NeuralAI SmolLM2-360M SFT v16 + DPO v16 LoRA',
)
"

✨ Key Features & Capabilities

💬 Multimodal Chat & Agentic Intelligence

• High-Velocity Text Inference: Fast, local inference with deep context awareness.
• Deep Reasoning Mode: Integration of test-time compute and chain-of-thought reasoning for complex problem decomposition and error-free logic.
• Autonomous Agentic Workflows: Ability to operate as an agent—interacting with the browser, terminal, and third-party apps to complete end-to-end tasks with minimal supervision.
• Live S2S (Speech-to-Speech): Real-time voice interaction with an integrated microphone interface and fluid vocal responses.
• Identity Vault & Memory: Persistent user memory and rule constraints, ensuring NeuralAI remembers preferences, behavioral rules, and historical context.

💻 Developer & Engineering Tools

• Integrated Web Terminal: A fully functional, WebSocket-driven terminal embedded directly in the web UI for immediate environment control.
• File Workspace: An in-browser IDE experience allowing users to browse directories, read, and write code seamlessly.
• Code Execution & Sandbox: Secure environment for the model to execute and test code on the fly.

🔐 Authentication & Access Tiers

• Founder Mode: Ultimate root-level access and system control.
• Maestro Student Portal: Tiered access for educational and collaborative development.
• Guest Preview: Frictionless instant access for testing the system without an account.

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🏋️ Model Training & Fine-Tuning (DPO)

NeuralAI is continuously learning and improving through rigorous Direct Preference Optimization (DPO).

Training Pipeline

# Example of the DPO alignment configuration used in NeuralAI
dpo_config = DPOConfig(
    beta=0.1,
    learning_rate=5e-5,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    max_length=1024,
    max_prompt_length=512,
)

• Dataset Expansions: The dataset is aggressively expanded to include advanced reasoning, complex mathematics, logical deduction, creative writing, and system debugging.
• Behavioral Alignment: NeuralAI is aligned using Gemini-style behavioral principles—prioritizing safety, structured reasoning, helpful conversational flow, and transparent step-by-step explanations. Training enforces clear Markdown formatting, code-first responses, and rejection of boilerplate or overly verbose outputs.
• Model Drift Monitoring: Continuous evaluation against previous checkpoints to ensure response quality and consistency never regress.

Latest Alignment Run: v15.0

• Training samples: 597 (expanded DPO preference pairs)
• Epochs: 3
• Steps: 450
• Final training loss: 0.305
• Reward margin: improved from ~0.5 → ~3.5 (model strongly prefers chosen responses)
• Hardware: Apple Silicon MPS (MacBook Air M4)
• Run duration: 730.5s (~12m 11s)
• Completed: 2026-07-11 20:00 UTC
• Adapter: live on Hugging Face at Subject-Emu-5259/NeuralAI

The v15 dataset (data/train_dpo_v15.jsonl) was generated by expanding the template pools in training/build_dataset_v15.py from 302 → 597 unique preference pairs covering code correctness, logic, reasoning, debugging, and multi-step tasks.

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📸 Brand & UI Gallery

(UI screenshots showcase the beautiful dark mode interface, the terminal integration, and the NeuralDrive file explorer.)

<!-- Example Frontend UI Component Structure -->
<div class="neural-chat-container">
  <div class="message-bubble ai-response">
    NeuralAI: System optimal. Ready for execution.
  </div>
</div>

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🗺️ Implementation Roadmap

• ✅ Phase 1: Alignment - DPO training for Founder context and optimal engineering tone.
• ✅ Phase 2: NeuralDrive - Deployment of the Cloud Storage File Server.
• ✅ Phase 3: Terminal UI - Integrated command-line access within the browser.
• ✅ Phase 4: Live S2S - High-velocity Live Speech-to-Speech conversations.
• ✅ Phase 5: "Founder Mode" - Enhancements to vocal profile and streamlined UI.
• ✅ Phase 6: Frontend Polish - Dark themes, real-time code execution display, UI stability.
• ✅ Phase 7: Diffusion Integration - Implementation of Text2Img & Img2Img capabilities.
• 🚀 Phase 8: Knowledge Graph & Agentic Autonomy - Advanced long-term memory for cross-project context, "Supermemory" features, and fully autonomous task execution.

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🎯 Future Vision: The Software Transition

NeuralAI is evolving from a workspace-bound assistant into a standalone, downloadable intelligence environment.

Project Code Name: NeuralLabs (Working Title) Vision: A local-first, AI-native operating environment that integrates the Agentic Orchestrator, World-Brain, and NeuralDrive into a seamless desktop experience—similar to the "Codex" model but expanded into a full cognitive workspace.

🚀 Roadmap Addition: The NeuralLabs Shift

• NeuralLabs v1 (Client): Development of a cross-platform wrapper (Electron/Tauri) for the NeuralAI interface.
• NeuralLabs v2 (Edge): Local model execution (Llama/Mistral) as a fallback for the cloud-based NeuralAI core.
• NeuralLabs v3 (Eco): Plugin architecture allowing third-party "Neural-Skills" to be installed as standalone apps.

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👨‍💻 The Developer & Architect

De'Andrew Preston Harris (D. Harris / Dre) Founder & Architect of NeuralAI

A dedicated software engineer, thinker, and builder from West Memphis, AR. De'Andrew is currently pursuing an AAS in AI Software Engineering at Maestro College. NeuralAI is the culmination of his[...]

• Location: Memphis, TN / West Memphis, AR
• Vision: Building the future of private, high-performance generative AI.
• LinkedIn | GitHub

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Built with precision and discipline by De'Andrew Preston Harris.

CURRENT VERSION: v7.3.2 (The Pluggable Engine)

• Model Alignment: DPO v15.0 Aligned (597 pairs, Logic, Debugging, Reasoning)
• Inference: llmster (LM Studio headless) — 258MB RAM vs 5GB PyTorch
• Last Maintenance: July 14, 2026

Your tone is technical, concise, and professional. You prioritize system stability and cleanliness above all else.

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🚀 Deployment

NeuralAI ships with a pluggable backend that separates the web UI from the inference engine.

Quick Start (llmster — recommended)

# 1. Install llmster (one-time)
curl -fsSL https://lmstudio.ai/install.sh | bash
export PATH="$HOME/.lmstudio/bin:$PATH"

# 2. Download model
lms import /path/to/SmolLM2-360M-Instruct-Q4_K_M.gguf --user-repo "bartowski/SmolLM2-360M-Instruct-GGUF" -y
lms load smollm2-360m-instruct -y --identifier smollm2

# 3. Start inference server
lms server start --port 1234

# 4. Start NeuralAI
cd NeuralAI
LLM_BACKEND=lmstudio LLM_API_URL=http://localhost:1234/v1 LLM_MODEL=smollm2 \
  python3 services/neural_core_service.py

Containerized Deployments

Deployment	Dockerfile	Stack	Status
Gradio Demo	gradio_space/Dockerfile	Gradio 6.x chat UI	✅ Built & deployed
Flask Web Chat	webui_space/Dockerfile	Flask + neural_core_service.py	🚀 Ready for Railway

• Adapter source: Subject-Emu-5259/NeuralAI — auto-pulled on startup via snapshot_download.
• GitHub → HF sync: .github/workflows/sync_to_huggingface.yml uploads only the LoRA adapter on every push to master.

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🌌 NeuralAI Project Manifest

NeuralAI is the intelligence core that powers the ecosystem.

🔗 Ecosystem Integration

The standalone software implementation of this core is NeuralLabs: 👉 https://github.com/Subject-Emu-5259/NeuralLabs

Software Downloads: The latest beta builds (v0.1-Beta) of NeuralLabs are available at: 👉 https://zo.pub/deandrewharris/neurallabs-beta

NeuralAI → Hugging Face sync is live