Subject-Emu-5259
/

NeuralAI

@@ -1,96 +1,265 @@
 ---
-base_model: HuggingFaceTB/SmolLM2-360M-Instruct
 library_name: peft
-model_name: NeuralAI
-model_type: adapter
 license: apache-2.0
-language:
-- en
 tags:
-- text-generation
-- dpo
-- lora
-- peft
-- smollm2
-- reasoning
-- code-generation
-- debugging
-- multi-step-reasoning
-- edge-ai
-pipeline_tag: text-generation
-inference:
-  parameters:
-    max_new_tokens: 512
-    temperature: 0.7
-    top_p: 0.95
-    repetition_penalty: 1.1
 ---
-# NeuralAI v15.0 — DPO-Aligned LoRA Adapter
-NeuralAI is a DPO-aligned LoRA adapter for [SmolLM2-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct), fine-tuned for expert-level reasoning, code generation, debugging, and multi-step logic tasks.
-## Highlights
-- **597 DPO preference pairs** covering code correctness, logic, reasoning, debugging, and multi-step tasks
-- **Reward margin**: improved from ~0.5 to ~3.5 (model strongly prefers chosen responses)
-- **Final training loss**: 0.305
-- **Edge-optimized**: Runs on CPU with 4GB RAM — no GPU required
-- **Gemini-style alignment**: Helpful, structured, conversational tone with step-by-step explanations
-## Quick start
 ```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from peft import PeftModel
-base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-360M-Instruct")
-tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-360M-Instruct")
-model = PeftModel.from_pretrained(base_model, "Subject-Emu-5259/NeuralAI")
-messages = [{"role": "user", "content": "Write a Python function to check API health."}]
-inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
-output = model.generate(inputs, max_new_tokens=256, temperature=0.7, top_p=0.95)
-print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))
 ```
-## Training details
-| Parameter | Value |
-|---|---|
-| Base model | HuggingFaceTB/SmolLM2-360M-Instruct |
-| Method | DPO (Direct Preference Optimization) |
-| Dataset | 597 preference pairs (v15 expanded) |
-| Epochs | 3 |
-| Steps | 450 |
-| Final loss | 0.305 |
-| Reward margin | ~3.5 |
-| LoRA rank | 16 |
-| Hardware | Apple Silicon MPS (MacBook Air M4) |
-| Duration | ~12 minutes |
-| Completed | 2026-07-11 |
-## Framework versions
-- PEFT: 0.17.1
-- TRL: 0.24.0
-- Transformers: 4.57.6
-- PyTorch: 2.8.0
-## Use cases
-- **Code generation and debugging**: Multi-step reasoning for code correctness
-- **Logic and math**: Complex problem decomposition
-- **Edge deployment**: CPU-optimized for local/private AI
-- **Agentic workflows**: Tool-use and multi-step task execution
-## Citation
-```bibtex
-@inproceedings{rafailov2023direct,
-    title        = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
-    author       = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
-    year         = 2023,
-    booktitle    = {NeurIPS 2023},
-}
 ```

 ---
+language:
+  - en
 library_name: peft
 license: apache-2.0
 tags:
+  - lora
+  - conversational
+  - text-generation
+  - peft
+  - smollm2
+  - dpo
+  - fine-tuned
+model_id: Subject-Emu-5259/NeuralAI
+base_model: HuggingFaceTB/SmolLM2-360M-Instruct
+inference: false
+---
+# 🧠 NeuralAI: The Generative AI Engine
+<img src="neuralai_banner.svg" alt="NeuralAI - Your AI. On your hardware. In your browser." />
+## 📊 Repository Composition
+| Language | Percentage |
+| --- | --- |
+| Python | 71.1% |
+| HTML | 13.0% |
+| JavaScript | 12.4% |
+| CSS | 2.6% |
+| Shell | 0.4% |
+| Jupyter Notebook | 0.3% |
+| Jinja | 0.2% |
+**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\[...\]
+---
+## 🌟 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.
 ---
+## 🛠️ 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` (DPO v15.0 Aligned for logic, math, multi-step reasoning, and debugging)
+- **Inference Engine**: [llmster](https://lmstudio.ai/docs/cli) (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 | Legacy/development mode |
+```bash
+# 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.
+---
+## ✨ 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.
+---
+## 🏋️ Model Training & Fine-Tuning (DPO)
+NeuralAI is continuously learning and improving through rigorous **Direct Preference Optimization (DPO)**.
+### Training Pipeline
 ```python
+# 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`](https://huggingface.co/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.
+---
+## 📸 Brand & UI Gallery
+*(UI screenshots showcase the beautiful dark mode interface, the terminal integration, and the NeuralDrive file explorer.)*
+```html
+<!-- Example Frontend UI Component Structure -->
+<div class="neural-chat-container">
+  <div class="message-bubble ai-response">
+    NeuralAI: System optimal. Ready for execution.
+  </div>
+</div>
 ```
+---
+## 🗺️ 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.
+---
+## 🎯 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.
+---
+## 👨‍💻 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](https://www.linkedin.com/in/deandrewharris94/) | [GitHub](https://github.com/Subject-Emu-5259)
+---
+*Built with precision and discipline by De'Andrew Preston Harris.*
+### CURRENT VERSION: v7.2 (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 12, 2026
+Your tone is technical, concise, and professional. You prioritize system stability and cleanliness above all else.
+---
+## 🚀 Deployment
+NeuralAI ships with a pluggable backend that separates the web UI from the inference engine.
+### Quick Start (llmster — recommended)
+```bash
+# 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`](https://huggingface.co/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`.
+---
+# 🌌 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](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](https://zo.pub/deandrewharris/neurallabs-beta)**
+# NeuralAI → Hugging Face sync is live