gemma-3-tamilnadu_sample

Gemma-3-1B Tamilnadu Sample

Model Description

This model is a fine-tuned version of Gemma-3-1B-IT specifically trained on Tamil Nadu personas and cultural context. It understands and responds with authentic Tamil Nadu cultural knowledge, mixing Tamil and English naturally, and demonstrates deep understanding of Tamil traditions, festivals, language, and regional nuances.

Key Features

• 🎯 Tamil Nadu Focused: Trained exclusively on personas from Tamil Nadu
• 🗣️ Bilingual: Natural code-mixing of Tamil and English
• 🎭 Cultural Awareness: Deep understanding of Tamil festivals, traditions, and customs
• 🛡️ Safety Aligned: Includes safety fine-tuning to refuse harmful requests
• 📚 Multi-Stage Training: SFT → Instruction Tuning → DPO → Safety

Training Details

Training Data

• Primary Dataset: Nemotron-Personas-India (en_IN split, Tamil Nadu only)
• Personas: ~20,000 Tamil Nadu personas
• Instructions: Tamil Nadu-specific + general knowledge mix
• DPO Pairs: Cultural preference alignment
• Safety Examples: Tamil Nadu context-aware safety responses

Training Stages

1. Stage 1 - Supervised Fine-Tuning (SFT)

   • Dataset: Tamil Nadu personas from Nemotron-Personas-India
   • Steps: 3000
   • Focus: Learning to roleplay as diverse Tamil Nadu personas

2. Stage 2 - Instruction Tuning

   • Dataset: Tamil Nadu instructions + general knowledge
   • Steps: 500
   • Mix Ratio: 80% Tamil Nadu, 20% General

3. Stage 3 - DPO (Direct Preference Optimization)

   • Dataset: Tamil Nadu preference pairs
   • Steps: 150
   • Beta: 0.1
   • Focus: Aligning responses with Tamil cultural preferences

4. Stage 4 - Safety Fine-Tuning

   • Dataset: Safety examples with Tamil Nadu context
   • Steps: 50
   • Focus: Ethical responses, anti-discrimination, cultural sensitivity

Training Configuration

• Base Model: unsloth/gemma-3-1b-it
• LoRA Rank: 32
• LoRA Alpha: 32
• Max Sequence Length: 2048
• Quantization: 4-bit
• Training Framework: Unsloth + TRL

Usage

Basic Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "naazimsnh02/gemma-3-tamilnadu_sample"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example prompt
messages = [
    {"role": "user", "content": "Tell me about Pongal festival in Tamil Nadu"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    inputs,
    max_new_tokens=256,
    temperature=0.7,
    top_p=0.9,
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

With Unsloth (Faster Inference)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="naazimsnh02/gemma-3-tamilnadu_sample",
    max_seq_length=2048,
    dtype=None,
    load_in_4bit=True,
)

FastLanguageModel.for_inference(model)

# Generate response
messages = [{"role": "user", "content": "Vanakkam! How are you?"}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

• Language: Primarily English with Tamil code-mixing; not fluent in pure Tamil text generation
• Geographic Scope: Optimized for Tamil Nadu context; may not generalize well to other Indian states
• Persona Bias: Training focused on specific demographic distributions from the dataset
• Cultural Nuances: May not capture all sub-regional variations within Tamil Nadu
• Safety: While safety-tuned, may still generate inappropriate content in edge cases

Ethical Considerations

• Anti-Discrimination: Model is trained to reject caste-based, religious, or regional discrimination
• Cultural Sensitivity: Respects Tamil Nadu's diverse communities and traditions
• Bias Mitigation: Includes safety fine-tuning to promote equality and respect
• Responsible Use: Should not be used to generate harmful, discriminatory, or misleading content

Training Infrastructure

• GPU: NVIDIA T4 (Google Colab)
• Training Time: ~10 hours (full pipeline)
• Framework: Unsloth + Transformers + TRL
• Optimization: 4-bit quantization, LoRA adapters, gradient checkpointing

Citation

If you use this model, please cite:

@misc{tamil-nadu-gemma-2025,
  title={Tamil Nadu Cultural AI Model based on Gemma-3-1B-IT},
  author={Syed Naazim Hussain},
  year={2025},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/naazimsnh02/gemma-3-tamilnadu_sample}}
}

Acknowledgments

• Base Model: Google's Gemma-3-1B-IT via Unsloth
• Dataset: NVIDIA's Nemotron-Personas-India
• Framework: Unsloth for efficient training
• Community: Tamil Nadu's rich cultural heritage

License

This model inherits the Gemma license from the base model. Please review the license terms before use.

Contact

For questions, issues, or feedback, please open an issue on the model repository.

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Note: This model is designed for research and educational purposes. Always verify outputs for accuracy and cultural appropriateness.