---
license: apache-2.0
language:
- en
- sa
- hi
- mr
- gu
- ta
- te
base_model:
- HuggingFaceTB/SmolLM3-3B-Base
---
# 🕉️ 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

```python
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)