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- ---
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- base_model: HuggingFaceTB/SmolLM2-360M-Instruct
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- library_name: peft
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- pipeline_tag: text-generation
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- tags:
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- - base_model:adapter:HuggingFaceTB/SmolLM2-360M-Instruct
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- - lora
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- - transformers
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- ---
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-
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
 
 
 
 
 
 
 
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
 
 
 
 
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 
 
 
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
 
 
 
 
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
 
 
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- [More Information Needed]
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- #### Factors
 
 
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
 
 
 
 
 
 
 
 
 
 
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- ### Results
 
 
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- [More Information Needed]
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- #### Summary
 
 
 
 
 
 
 
 
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
 
 
 
 
 
 
 
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
 
 
 
 
 
 
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
 
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- ### Compute Infrastructure
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- [More Information Needed]
 
 
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- #### Hardware
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- [More Information Needed]
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- #### Software
 
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- [More Information Needed]
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- ## Citation [optional]
 
 
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- [More Information Needed]
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- **APA:**
 
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- [More Information Needed]
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
 
 
 
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- [More Information Needed]
 
 
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
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- ### Framework versions
 
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- - PEFT 0.19.0
 
 
 
 
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+ # 🧠 NeuralAI: The Generative AI Engine
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ <img src="neuralai_banner.svg" alt="NeuralAI - Your AI. On your hardware. In your browser." />
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+ ## 📊 Repository Composition
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+ | Language | Percentage |
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+ | --- | --- |
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+ | Python | 71.1% |
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+ | HTML | 13.0% |
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+ | JavaScript | 12.4% |
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+ | CSS | 2.6% |
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+ | Shell | 0.4% |
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+ | Jupyter Notebook | 0.3% |
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+ | Jinja | 0.2% |
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+ **The High-Velocity Model for Your Entire Vibe Stack**
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+ 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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+ ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## 🌟 Vision & Manifesto
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+ 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\[...\]
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+ 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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+ ---
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+ ## 🛠️ Tech Stack & Architecture (v7.1-alpha)
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+ NeuralAI is built on a high-performance, containerized architecture that marries local inference with cloud-grade storage.
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+ ### Core Stack
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+ - **Core Model**: `SmolLM2-360M-Instruct` (DPO v15.0 Aligned for logic, math, multi-step reasoning, and debugging)
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+ - **Vocal Identity**: Andrew (Warm/Multilingual) - Optimized for Live Speech-to-Speech (S2S)
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+ - **Backend Framework**: Python / Flask (Core Service)
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+ - **Storage & Database**: SQLite3 (Metadata) + Nextcloud Hub via NeuralCloud WebDAV Client (NeuralDrive)
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+ - **Inference Engine**: PyTorch (CPU/Edge Optimized)
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+ - **Frontend UI**: Vanilla JS, HTML5, CSS3 with an advanced Dark Mode layout
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+ ### Core Architectural Pillars
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+ 1. **NeuralAI Core**: Handles chat state, direct model inference, terminal session proxying, and tool orchestration.
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+ 2. **NeuralDrive (Cloud Storage)**: The intelligent data layer for all projects, featuring isolated user storage, automatic versioning, and semantic mapping.
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+ 3. **Diffusion Engine**: An integrated generative diffusion layer for producing visual branding assets, UI mockups, and visual logic maps.
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+ 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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+ ---
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+ ## ✨ Key Features & Capabilities
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+ ### 💬 Multimodal Chat & Agentic Intelligence
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+ - **High-Velocity Text Inference**: Fast, local inference with deep context awareness.
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+ - **Deep Reasoning Mode**: Integration of test-time compute and chain-of-thought reasoning for complex problem decomposition and error-free logic.
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+ - **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.
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+ - **Live S2S (Speech-to-Speech)**: Real-time voice interaction with an integrated microphone interface and fluid vocal responses.
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+ - **Identity Vault & Memory**: Persistent user memory and rule constraints, ensuring NeuralAI remembers preferences, behavioral rules, and historical context.
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+ ### 💻 Developer & Engineering Tools
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+ - **Integrated Web Terminal**: A fully functional, WebSocket-driven terminal embedded directly in the web UI for immediate environment control.
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+ - **File Workspace**: An in-browser IDE experience allowing users to browse directories, read, and write code seamlessly.
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+ - **Code Execution & Sandbox**: Secure environment for the model to execute and test code on the fly.
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+ ### 🔐 Authentication & Access Tiers
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+ - **Founder Mode**: Ultimate root-level access and system control.
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+ - **Maestro Student Portal**: Tiered access for educational and collaborative development.
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+ - **Guest Preview**: Frictionless instant access for testing the system without an account.
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+ ---
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+ ## 🏋️ Model Training & Fine-Tuning (DPO)
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+ NeuralAI is continuously learning and improving through rigorous **Direct Preference Optimization (DPO)**.
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+ ### Training Pipeline
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+ ```python
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+ # Example of the DPO alignment configuration used in NeuralAI
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+ dpo_config = DPOConfig(
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+ beta=0.1,
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+ learning_rate=5e-5,
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+ per_device_train_batch_size=4,
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+ gradient_accumulation_steps=4,
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+ max_length=1024,
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+ max_prompt_length=512,
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+ )
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+ ```
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+ - **Dataset Expansions**: The dataset is aggressively expanded to include advanced reasoning, complex mathematics, logical deduction, creative writing, and system debugging.
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+ - **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.
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+ - **Model Drift Monitoring**: Continuous evaluation against previous checkpoints to ensure response quality and consistency never regress.
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+ ### Latest Alignment Run: v15.0
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+ - **Training samples**: 597 (expanded DPO preference pairs)
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+ - **Epochs**: 3
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+ - **Steps**: 450
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+ - **Final training loss**: `0.305`
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+ - **Reward margin**: improved from `~0.5` → `~3.5` (model strongly prefers chosen responses)
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+ - **Hardware**: Apple Silicon MPS (MacBook Air M4)
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+ - **Run duration**: `730.5s` (~12m 11s)
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+ - **Completed**: `2026-07-11 20:00 UTC`
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+ - **Adapter**: live on Hugging Face at [`Subject-Emu-5259/NeuralAI`](https://huggingface.co/Subject-Emu-5259/NeuralAI)
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+ > 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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+ ---
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+ ## 📸 Brand & UI Gallery
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+ *(UI screenshots showcase the beautiful dark mode interface, the terminal integration, and the NeuralDrive file explorer.)*
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+ ```html
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+ <!-- Example Frontend UI Component Structure -->
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+ <div class="neural-chat-container">
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+ <div class="message-bubble ai-response">
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+ NeuralAI: System optimal. Ready for execution.
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+ </div>
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+ </div>
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+ ```
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+ ---
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+ ## 🗺️ Implementation Roadmap
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+ - **Phase 1: Alignment** - DPO training for Founder context and optimal engineering tone.
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+ - ✅ **Phase 2: NeuralDrive** - Deployment of the Cloud Storage File Server.
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+ - ✅ **Phase 3: Terminal UI** - Integrated command-line access within the browser.
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+ - ✅ **Phase 4: Live S2S** - High-velocity Live Speech-to-Speech conversations.
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+ - ✅ **Phase 5: "Founder Mode"** - Enhancements to vocal profile and streamlined UI.
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+ - ✅ **Phase 6: Frontend Polish** - Dark themes, real-time code execution display, UI stability.
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+ - ✅ **Phase 7: Diffusion Integration** - Implementation of Text2Img & Img2Img capabilities.
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+ - 🚀 **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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+ ---
 
 
 
 
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+ ## 🎯 Future Vision: The Software Transition
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+ NeuralAI is evolving from a workspace-bound assistant into a standalone, downloadable intelligence environment.
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+ **Project Code Name**: `NeuralLabs` (Working Title)
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+ **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.
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+ ### 🚀 Roadmap Addition: The NeuralLabs Shift
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+ - **NeuralLabs v1 (Client)**: Development of a cross-platform wrapper (Electron/Tauri) for the NeuralAI interface.
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+ - **NeuralLabs v2 (Edge)**: Local model execution (Llama/Mistral) as a fallback for the cloud-based NeuralAI core.
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+ - **NeuralLabs v3 (Eco)**: Plugin architecture allowing third-party "Neural-Skills" to be installed as standalone apps.
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+ ---
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+ ## 👨‍💻 The Developer & Architect
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+ **De'Andrew Preston Harris** (D. Harris / Dre)
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+ *Founder & Architect of NeuralAI*
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+ 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\[...\]
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+ - **Location:** Memphis, TN / West Memphis, AR
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+ - **Vision:** Building the future of private, high-performance generative AI.
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+ - [LinkedIn](https://www.linkedin.com/in/deandrewharris94/) | [GitHub](https://github.com/Subject-Emu-5259)
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+ ---
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+ *Built with precision and discipline by De'Andrew Preston Harris.*
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+ ### CURRENT VERSION: v7.1-alpha (The Agentic Operator)
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+ - **Model Alignment**: DPO v15.0 Aligned (597 pairs, Logic, Debugging, Reasoning)
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+ - **Last Maintenance**: July 11, 2026
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+ Your tone is technical, concise, and professional. You prioritize system stability and cleanliness above all else.
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+ ---
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+ ## 🚀 Deployment
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+ NeuralAI ships two containerized deployments, both pulling the LoRA adapter from Hugging Face at runtime:
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+ | Deployment | Dockerfile | Stack | Status |
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+ | --- | --- | --- | --- |
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+ | **Gradio Demo** | `gradio_space/Dockerfile` | Gradio 6.x chat UI | ✅ Built & deployed (Metal builder, healthcheck `/`) |
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+ | **Flask Web Chat** | `webui_space/Dockerfile` | Flask + `neural_core_service.py` | 🚀 Ready for Railway (`railway.json`) |
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+ - **Adapter source**: [`Subject-Emu-5259/NeuralAI`](https://huggingface.co/Subject-Emu-5259/NeuralAI) — auto-pulled on startup via `snapshot_download`, so retraining + pushing updates the live model on next restart.
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+ - **8-bit quantization** (`QUANTIZE=1`) keeps the 360M model under the 512 MB free-tier RAM limit.
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+ - **GitHub → HF sync**: `.github/workflows/sync_to_huggingface.yml` uploads only the LoRA adapter (not the 1.5 GB repo) on every push to `master`.
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+ ---
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+ # 🌌 NeuralAI Project Manifest
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+ NeuralAI is the intelligence core that powers the ecosystem.
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+ ## 🔗 Ecosystem Integration
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+ The standalone software implementation of this core is **NeuralLabs**:
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+ 👉 [https://github.com/Subject-Emu-5259/NeuralLabs](https://github.com/Subject-Emu-5259/NeuralLabs)
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+ **Software Downloads**:
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+ The latest beta builds (v0.1-Beta) of NeuralLabs are available at:
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+ 👉 **[https://zo.pub/deandrewharris/neurallabs-beta](https://zo.pub/deandrewharris/neurallabs-beta)**
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+ # NeuralAI → Hugging Face sync is live