UXI-LLM

A next-generation, modular, symbolic + neural hybrid large language model built to solve modern AI's biggest pain points.

🔥 Key Features

• 🧠 Hybrid AI Core: Integrates neural transformers with symbolic reasoning.
• 🧩 True Modularity: Swap or extend any model component (tokenizer, attention, reasoning).
• 🖥️ Fully Local Compatible: Train and run offline on local machines.
• 🔌 Plugin System: Extend capabilities with hot-swappable modules.
• 📡 API Ready: Full REST, WebSocket, and CLI interfaces.
• 📊 Evaluation Tools: Benchmarks, logic tests, and memory diagnostics included.

🚀 Why UXI-LLM?

Modern LLMs are huge, closed, cloud-dependent, and rigid. UXI-LLM is the opposite:

• Local-first, open, and customizable.
• Built for hackers, researchers, and devs who want control.
• Designed to evolve with the community.

📦 Tech Stack

• Python 3.10+
• PyTorch (for neural layers)
• SymPy / Z3 / MiniKanren (for symbolic logic)
• FastAPI (for API layer)
• YAML (for modular configs)
• Docker (for CI/infra)

📂 Project Layout (Preview)

/core         - neural model code
/symbolic     - rule engine, logic DSL
/plugins      - custom extensions
/api          - FastAPI + CLI tools
/configs      - YAML config files
/tests        - unit + integration tests
/docs         - Markdown docs + architecture

🧠 Symbolic Reasoning Capabilities

UXI-LLM can:

• Apply user-defined logic rules to guide generations
• Mix symbolic + statistical inference at runtime
• Embed reasoning modules inside transformer attention

⚙️ Installation

# Requirements
Python 3.10+
pip install -r requirements.txt

🛠️ Development Status

Currently in heavy development. MVP will support:

• Training on small local datasets
• Interoperable plugin injection
• Symbolic parsing + attention hooks

📄 License

MIT — Free for all use.

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