RavinduSen/JaneGPT-v2

JaneGPT v2 — Intent Classification Model

A lightweight, fast, and accurate intent classification model built from scratch for virtual assistant command understanding.

7.8M parameters | 22 intent classes | 88.6% validation accuracy | ~17ms inference on GPU

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Why I Built This

I'm building JANE — a fully offline, privacy-first AI voice assistant. Llama 3 8B was causing 10–22 second delays for simple commands like "turn up the volume."

That's not a voice assistant. That's a waiting game.

So I designed JaneGPT v2 from scratch — a model that does exactly one job, does it fast, and runs on consumer hardware without any cloud dependency.

Model Details

Architecture Decisions & Why

Supported Intents (22 classes)

Performance

Quick Start

Installation

git clone https://huggingface.co/RavinduSen/JaneGPT-v2
cd JaneGPT-v2
pip install -r requirements.txt

Basic Usage

from classifier import JaneGPTClassifier

classifier = JaneGPTClassifier()

intent, confidence = classifier.predict("turn up the volume")
print(f"Intent: {intent}, Confidence: {confidence:.2%}")
# Output: Intent: volume_up, Confidence: 86.10%

intent, confidence = classifier.predict("open chrome")
print(f"Intent: {intent}, Confidence: {confidence:.2%}")
# Output: Intent: app_launch, Confidence: 98.10%

With Conversation Context

intent, confidence = classifier.predict(
    "not enough",
    context={"last_intent": "volume_up"}
)
# Output: Intent: volume_up, Confidence: 79.00%

Training Setup

Limitations

• —Intent classification only — does not generate text
• —22 classes — commands outside supported set classified as chat
• —English only
• —Optimized for short inputs (1–15 words)
• —No entity extraction — returns intent label only

Use Cases

• —Virtual assistant command routing
• —Smart home intent classification
• —Voice command understanding
• —Chatbot intent detection
• —Edge device deployment (small enough for embedded systems)

Part of the JANE Project

This model is the intelligence core of JANE — a fully offline, privacy-first AI voice assistant.

🔗 JANE AI Assistant on GitHub 🔗 JaneGPT-v2 on GitHub

Created By

Ravindu Senanayake — Computer Science Undergraduate, Sri Lanka

Built from scratch — architecture, tokenizer, and training pipeline designed and implemented by the author.