Nexa Auto

Nexa Auto is your all-in-one, session-aware CLI and TUI tool for fine-tuning Hugging Face-compatible models. It makes secure, repeatable, and portable LLM fine-tuning as simple as following a guided workflow—no notebooks, no cloud lock-in, no headaches.

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Why Nexa Auto?

• CLI & TUI: Choose your interface—modern terminal UI (Go/BubbleTea) or a sleek CLI (Python Rich).
• Secure by design: Your Hugging Face token is kept in memory, never written to disk.
• Guided workflow: Select model, dataset, and output step-by-step.
• Hardware smart: Detects and displays your CPU/GPU resources.
• LoRA/PEFT ready: Efficient adapter-based fine-tuning out of the box.
• Extensible: Modular for new training modes, hardware checks, and logging.

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Installation

Requirements:

• Python 3.8+ (backend & CLI)
• Go 1.18+ (for TUI, optional)
• CUDA GPU (recommended)
• Hugging Face access token

Get started:

git clone https://github.com/your-org/nexa-auto.git
cd nexa-auto
pip install -r requirements.txt
# (Optional) For TUI:
cd go_cli
go mod tidy

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Quickstart

1. Start the session server:

   python session_server.py

2. Launch your interface:
   • CLI: python cli.py
   • TUI: cd go_cli && go run main.go
3. Follow the prompts:
   • Enter your Hugging Face token (first run)
   • Pick your base model and dataset
   • Name your output
   • Confirm hardware
   • Start training!
4. Monitor progress:
   Watch logs and training status live in your chosen interface.

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How It Works

• Session Server: Local FastAPI server keeps your Hugging Face token safe in memory.
• CLI/TUI: Guides you through model/dataset/output selection and training.
• Trainer Backend: Handles model loading, tokenization, LoRA/PEFT, and artifact saving.

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Project Structure

nexa_auto/
├── cli.py              # Python CLI (Rich)
├── session_server.py   # Secure token server (FastAPI)
├── trainer_server.py   # Training backend (REST)
├── go_cli/             # Go TUI (BubbleTea)
├── doc.md              # Technical docs
└── README.md

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Security

• No secrets on disk: Tokens are only in memory, encrypted.
• Local-only: Session server listens only on localhost.
• Clear on exit: Tokens wiped at session end or on request.

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Example Workflow

python session_server.py
python cli.py
# or
cd go_cli && go run main.go

• Authenticate with your Hugging Face token.
• Select model, dataset, and output name.
• Confirm hardware.
• Start and monitor training.
• Retrieve your fine-tuned model—ready for Hugging Face Hub!

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Extending Nexa Auto

• Add new training modes: edit remote.py and update the UI.
• Add hardware checks: extend hardware.py.
• Add logging/metrics: hook into logging.py.

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Documentation

See doc.md for full technical details, architecture, and extension notes.

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Contributing

We welcome issues, feature requests, and PRs!
Open an issue or pull request to get involved.

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License

MIT License. See LICENSE.

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