🌌 DeepQ
🤯 What is DeepQ?
DeepQ is an advanced deep reasoning language model created through the synergy between the QFamily architecture and the cutting-edge DeepRethink dataset. Designed to push the limits of context-rich inference, explanation generation, and reflective response modeling, DeepQ is the next evolution in human-like thought simulation.
It inherits the base architecture of gpt2-medium
and is fine-tuned with the DeepRethink dataset (kulia-moon/DeepRethink
), which focuses on multi-perspective reasoning, contradictory thought, question decomposition, and hypothetical situations — all geared towards cultivating a machine that rethinks before responding.
📦 Key Features
Feature | Description |
---|---|
🧠 DeepRethink Data | Trained on thousands of synthetic and real thought chains |
🧬 Cognitive Patterns | Simulates re-evaluation and critical thinking behaviors |
🏗 GPT2 Foundation | Built on openai-community/gpt2-medium |
🌎 Regional Scaling | Deploys across regions for low-latency use |
💬 Reflective Responses | Handles contradiction, dilemma, and uncertainty contexts |
🛠 Use Case Ready | Research, chatbots, simulators, tutoring systems, AI ethics discussions |
☁️ Multi-vendor Support | Optimized for deployment on Hugging Face, Vercel, AWS, GCP, Azure |
🚀 Streaming Compatible | Full support for SSE and WebSocket-based AI pipelines |
📚 Licensing | MIT license, open and production-friendly |
🚀 Deployments
Region | Vendor | Endpoint | Deployment Badge |
---|---|---|---|
US East (VA) | Hugging Face | US East | |
EU West (Ireland) | Hugging Face | EU West | |
Asia (Singapore) | Hugging Face | Asia | |
Global CDN | Vercel | Vercel CDN | |
US West (Oregon) | AWS | AWS | |
EU Central (Frankfurt) | AWS | AWS EU | |
Tokyo | GCP | GCP JP | |
Sydney | Azure | Azure AU | |
São Paulo | Hugging Face | Brazil | |
India (Mumbai) | Hugging Face | India | |
Canada (Montreal) | Hugging Face | Canada | |
Africa (Cape Town) | Hugging Face | Africa | |
Middle East (Bahrain) | Hugging Face | Middle East |
🧪 Use Cases
- AI Research: Foundation for studying multi-layered logic simulation and AI explainability
- Reflective Chatbots: For applications needing nuanced and multi-turn understanding
- Tutoring Systems: Where feedback loops and re-evaluation are essential
- Debate Engines: Model holds internal opposition to simulate conflict and resolution
- Philosophical AI: Explore cognitive dissonance, ethics, duality, and hypothetical constructs
- Medical/Ethical Simulators: With dilemma-aware prompts and double-sided scenarios
🧭 Quickstart
pip install transformers
from transformers import pipeline
qa = pipeline("text-generation", model="StableChatAI/DeepQ")
qa("Why do people sometimes change their beliefs?")
🌐 Links
- Model Card: https://huggingface.co/StableChatAI/DeepQ
- Dataset: https://huggingface.co/datasets/kulia-moon/DeepRethink
- Deploy Model: https://endpoints.huggingface.co/new?repository=StableChatAI/DeepQ
- GitHub: https://github.com/StableChatAI/DeepQ
- License: MIT
“DeepQ isn't just another language model — it's a new frontier of thought.” — QFamily Lab 🧪
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Base model
openai-community/gpt2-medium