sgattup/IndianCultureLLM

🇮🇳 Indian Culture LLM

A fine-tuned language model focused on Indian culture, history, philosophy, arts, and traditions — built to answer questions about one of the world's oldest and richest civilizations.

What This Model Knows

This model has been trained on high-quality instruction pairs covering:

• Hindu Mythology — Mahabharata, Ramayana, Puranas, key deities and their stories
• Indian Philosophy — Vedanta, Advaita, Yoga (Patanjali's 8 limbs), Bhakti movement, Jainism, Buddhism, Sikhism
• Classical Arts — Bharatanatyam, Kathak, Odissi, Kuchipudi, Manipuri, Mohiniyattam, Kathakali, Carnatic and Hindustani music, Raga system, Gharanas
• Festivals — Diwali, Holi, Navratri, Durga Puja, Onam, Pongal, Kumbh Mela, Rath Yatra, Thrissur Pooram, Garba/Dandiya
• Indian History — Indus Valley Civilization, Vedic period, Maurya and Gupta Empires, Mughal era, Bhakti movement, Independence movement, Partition
• Key Figures — Gandhi, Ambedkar, Ashoka, Chandragupta, Akbar, Shivaji, Tagore, Vivekananda, Ramanujan, Aryabhata, Tansen, MS Subbulakshmi, Ravi Shankar, Lata Mangeshkar, Mirabai, Kabir Das, Adi Shankaracharya
• Ayurveda & Wellness — Doshas, Panchakarma, Ayurvedic cooking, medicinal plants (Tulsi, Neem, Peepal)
• Regional Cuisine — North vs South Indian food, Mughal culinary contributions, Ayurvedic cooking, Indian spices
• Traditional Art Forms — Madhubani painting, Warli art, Pattachitra, Kolam/Rangoli, Tanjore painting
• Sacred Texts — Bhagavad Gita, Upanishads, Arthashastra, Thirukkural, Panchatantra, Natya Shastra
• Architecture & Heritage — Taj Mahal, Hampi, Nalanda University, Chola temples, Vastu Shastra

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "sgattup/IndianCultureLLM"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

ALPACA_PROMPT = """Below is a question or instruction about Indian culture.
Write a thoughtful, accurate, and detailed response.

### Instruction:
{}

### Response:
"""

def ask(question):
    prompt = ALPACA_PROMPT.format(question)
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.split("### Response:")[-1].strip()

print(ask("What is the significance of Diwali in Indian culture?"))
print(ask("Explain the eight limbs of Yoga according to Patanjali."))
print(ask("Who was Adi Shankaracharya and what did he teach?"))

Example Outputs

Q: What is a Raga in Indian classical music?

A Raga is the fundamental melodic framework of Indian classical music — not merely a scale but a complete musical personality with rules about which notes to use, how to ascend and descend, what mood it evokes, and what time of day it should be performed. Different ragas are associated with times of day: Bhairav at dawn, Yaman at dusk...

Q: Who was Dr. B.R. Ambedkar?

Dr. Bhimrao Ramji Ambedkar (1891–1956) was one of the most transformative figures in modern Indian history. Born into a Dalit family, he overcame severe discrimination to earn degrees from Columbia University and the London School of Economics. He was the principal architect of India's Constitution, which abolished untouchability and guaranteed fundamental rights to all citizens regardless of caste...

Model Details

Property	Value
Base Model	LLaMA 3.2 3B (unsloth/llama-3.2-3B-bnb-4bit)
Fine-tuning Method	QLoRA via Unsloth
LoRA Rank	16
Training Epochs	3
Dataset Size	75+ high-quality instruction pairs
Language	English
License	Apache 2.0

Limitations

• This is an early version trained on a relatively small dataset — responses are informative but may lack depth on niche topics
• Primarily covers mainstream Hindu/pan-Indian cultural topics; regional and tribal cultures are underrepresented
• Not a substitute for academic sources on complex historical or religious topics
• May reflect certain perspectives more than others given the training data

Future Plans

• Expand dataset to 2,000+ examples
• Add regional language support (Tamil, Telugu, Hindi, Bengali)
• Cover tribal and indigenous Indian cultures more deeply
• Add a HuggingFace Space for interactive demos
• Train a larger 7B version for improved depth

Training Code

Training code and dataset are available at: github.com/sai-educ/indian-culture-llm

Citation

If you use this model in your work, please cite:

@misc{IndianCultureLLM2026,
  author = {sgattup},
  title = {Indian Culture LLM},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/sgattup/IndianCultureLLM}
}