Subject-Emu-5259
/

NeuralAI

@@ -1,273 +1,96 @@
 ---
-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.
----
-### 🆕 What's New (v7.3.0)
-- **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).
-## ✨ 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: v7.3.0 (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 13, 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

 ---
+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},
+}
 ```