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--- |
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license: apache-2.0 |
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language: |
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- en |
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- sa |
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- hi |
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- mr |
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- gu |
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- ta |
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- te |
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base_model: |
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- HuggingFaceTB/SmolLM3-3B-Base |
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--- |
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# 🕉️ DeepShiva - AI Travel Companion for Indian Tourism & Wellness |
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Your intelligent guide to India's spiritual and cultural heritage |
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--- |
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## 🌟 Overview |
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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. |
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DeepShiva provides culturally-informed, spiritually-aware AI assistance that respects and preserves traditional knowledge while making it accessible to modern practitioners. |
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## 🔧 Technical Specifications |
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- **Base Model:** SmolLM3-3B-Base (3B parameters) |
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- **Fine-tuning Method:** QLoRA (Quantized Low-Rank Adaptation) |
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- **Training Type:** Unsupervised Fine-tuning |
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- **Architecture:** Transformer-based with specialized Indian cultural knowledge |
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- **Hardware:** Trained on AMD MI300 GPU |
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- **Model Size:** 3B parameters |
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--- |
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## 🎮 Try the Model |
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Experience DeepShiva through our interactive web interface: |
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- **Live Demo:** Try our Fine-tuned Model |
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- **Hugging Face Space:** Available for direct model interaction |
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- **API Access:** Available through Hugging Face Inference API |
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--- |
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## 🏃♂️ Quick Start |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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# Load the model and tokenizer |
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model_name = "Riddhish121/DeepShiva_Indian_Culture" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype=torch.float16, |
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device_map="auto" |
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) |
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# Example usage |
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prompt = "Guide me through a traditional yoga practice for beginners" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=200, temperature=0.7) |
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response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(response) |
