Riddhish121/DeepShiva_Indian_Culture

🕉️ DeepShiva - AI Travel Companion for Indian Tourism & Wellness

Your intelligent guide to India's spiritual and cultural heritage

🌟 Overview

DeepShiva is a specialized AI model designed to bridge the gap between modern travelers and India's rich spiritual traditions. Built on the robust foundation of SmolLM3-3B-Base, this model serves as your personal companion for exploring Indian tourism, wellness practices, yoga, Ayurveda, and ancient wisdom.

DeepShiva provides culturally-informed, spiritually-aware AI assistance that respects and preserves traditional knowledge while making it accessible to modern practitioners.

🔧 Technical Specifications

• —Base Model: SmolLM3-3B-Base (3B parameters)
• —Fine-tuning Method: QLoRA (Quantized Low-Rank Adaptation)
• —Training Type: Unsupervised Fine-tuning
• —Architecture: Transformer-based with specialized Indian cultural knowledge
• —Hardware: Trained on AMD MI300 GPU
• —Model Size: 3B parameters

🎮 Try the Model

Experience DeepShiva through our interactive web interface:

• —Live Demo: Try our Fine-tuned Model
• —Hugging Face Space: Available for direct model interaction
• —API Access: Available through Hugging Face Inference API

🏃‍♂️ Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load the model and tokenizer
model_name = "Riddhish121/DeepShiva_Indian_Culture"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Example usage
prompt = "Guide me through a traditional yoga practice for beginners"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)