🛡️ KVCHub — On-Device Privacy & High-Speed Neural Intelligence

Welcome to the official KVCHub Organization profile!

At KVCHub, we research and build ultra-lightweight, hardware-accelerated, zero-trust AI models. Our mission is to make advanced privacy redaction and neural intelligence run 100% locally on-device with zero cloud data leakage, zero subscription fees, and sub-millisecond execution speeds.

🚀 Flagship Models

🌟 Core Features & Breakthroughs

• 🔒 Zero Data Leakage (100% On-Device): Text never leaves local memory. Fully compliant with HIPAA, GDPR, and SOC2.
• ⚡ Sub-Millisecond Speed: Runs up to 130x faster than cloud network roundtrips to OpenAI or Google DLP.
• 🧪 Zero False Positives: Immunized against scientific constants (299,792,458 m/s), subnet masks (255.255.255.0), legal rules (12(b)(6)), and stock tickers ($182.50).
• 🌍 Zero-Shot International: Detects French, UK, German, and Japanese addresses, phones, and names zero-shot.
• 🌊 Real-Time Streaming: Sanitizes live streaming LLM outputs token-by-token on the fly.

⚡ Quickstart

Install the official Python package from PyPI:

pip install kava-privacy

Run PII redaction in 2 lines of Python:

from kava_privacy import KavaPrivacy

# Initialize KVCHub Privacy Engine
kp = KavaPrivacy(model_name="2.01M")

redacted_text, entities = kp.redact("Email me at jane.doe@gmail.com or call 555-123-4567.")
print(redacted_text)
# Output: "Email me at [EMAIL] or call [PHONE]."

Sanitize entire CSV, JSON, or Log files via CLI:

python3 kava_clean.py customer_data.csv -o clean_data.csv

📜 Principles & Open Source

All KVCHub models and software are released under the permissive MIT License. Free for commercial and open-source applications worldwide.

• PyPI Package: pypi.org/project/kava-privacy/
• Hugging Face Org: huggingface.co/KVCHub

Built with ❤️ by the KVCHub Engineering Team