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)

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]))