🚀 Gunrah Elite: Mini version EverestQ AI
⚡ Advanced AI Assistant | Mini Version of EverestQ
Built for intelligent reasoning, structured outputs, and real-world AI applications
🧠 Overview
Gunrah Elite is a high-performance, fine-tuned Large Language Model , designed as a mini version of EverestQ — a next-generation multimodal AI system.
It combines speed, efficiency, and structured intelligence to deliver powerful results across coding, research, and problem-solving tasks.
💡 Think of Gunrah as a lightweight EverestQ prototype — optimized for accessibility without sacrificing intelligence.
🏔️ About EverestQ (Parent Vision)
EverestQ is an advanced AI initiative focused on:
- 🌐 Multimodal Intelligence (Text + Vision + Audio)
- 🌍 Multilingual Understanding
- 🧠 Human-like Reasoning Systems
- ⚡ Scalable AI Infrastructure
Gunrah Elite represents Phase-1 execution of this larger vision.
📒 Model Details
| Feature | Description |
|---|---|
| 🧑💻 Developer | Rahul Chaube |
| 🏢 Organization | Artistic Impression |
| 🔧 Fine-tuned From | Gunrah-Core |
| 🌐 Language | English |
| 📜 License | Apache-2.0 |
🚀 Key Features
🧠 Structured Reasoning
- Step-by-step logical outputs
- Ideal for problem-solving & learning
💻 Developer Friendly
- Strong coding assistance
- Clean and structured responses
⚡ Optimized Performance
- 4-bit quantization
- Low memory usage
- Faster inference
🎯 Professional Tone
- Suitable for:
- Research work
- Business insights
- Technical writing
🧩 Modular Intelligence
- Designed as a building block for:
- EverestQ ecosystem
- Custom AI systems
💬 Usage
🔹 1. Quick Chat (UI)
Use the Hugging Face Inference Widget → start chatting instantly.
🔹 2. Transformers (Python)
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "oncody/gunrah-8b"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
input_text = "Explain AI in simple terms"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0]))