Indic-mobile

Indic-mobile

Indic-mobile is a 0.5B parameter language model built completely from scratch — no fine-tuning, no adapter on top of an existing checkpoint. Every weight was pretrained from zero, purpose-built for all 22 officially recognized Indian languages and designed for efficient deployment on mobile and edge devices.

🤗 GGUF quantized versions are available at mradermacher/Indic-mobile-GGUF

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Model Details

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Supported Languages

Indic-mobile covers all 22 languages recognized under the 8th Schedule of the Indian Constitution:

Assamese, Bengali, Bodo, Dogri, Gujarati, Hindi, Kannada, Kashmiri, Konkani, Maithili, Malayalam, Manipuri, Marathi, Nepali, Odia, Punjabi, Sanskrit, Santali, Sindhi, Tamil, Telugu, Urdu

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Why Indic-mobile?

India has 1.4 billion people and 22 officially recognized languages — yet most language models were never built with this diversity in mind. Indic-mobile is designed to change that:

• Built from scratch — not a fine-tune or adapter on an existing English-centric model
• Truly multilingual — trained across all 22 Indian languages from the ground up
• Mobile-first — 0.5B parameters means it runs efficiently on edge devices and smartphones
• Open source — weights, architecture, and everything else, freely available

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Usage

Load with 🤗 Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "RockySinghRajput/Indic-mobile"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prompt = "भारत एक विविधताओं से भरा देश है।"  # Example Hindi prompt
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Run with Ollama

ollama run hf.co/RockySinghRajput/Indic-mobile

Run with vLLM

vllm serve RockySinghRajput/Indic-mobile

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Model Architecture

• Architecture: Custom (trained from scratch)
• Parameters: 0.5B
• Precision: BF16
• Training: Pretrained from scratch (no base model used)
• Objective: Causal language modeling across 22 Indic languages

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Intended Uses

Direct Use

• Text generation in any of the 22 official Indian languages
• Multilingual Indic chatbots and assistants
• On-device / mobile NLP applications
• Low-resource language research and experimentation

Downstream Use

• Fine-tuning for specific Indic language tasks (classification, summarization, translation, QA)
• Integration into larger Indic NLP pipelines
• RAG (Retrieval-Augmented Generation) systems for Indian language content

Out-of-Scope Use

• High-stakes decision making without human oversight
• Generation of harmful, misleading, or abusive content in any language
• Tasks requiring deep factual accuracy without verification

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Bias, Risks, and Limitations

• As a small 0.5B model, it may struggle with complex reasoning or long-form generation compared to larger models
• Training data distribution across all 22 languages may not be perfectly balanced; lower-resource languages may underperform
• Like all language models, it may reflect biases present in the training data
• Not intended for use in safety-critical or high-stakes applications without further evaluation and fine-tuning

Recommendations

Users should evaluate the model on their specific use case and language before deployment, particularly for lower-resource Indic languages.

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Evaluation

Formal benchmarks are in progress. Community evaluations and feedback are welcome — please open a Discussion to share results!

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Citation

If you use Indic-mobile in your research or projects, please consider citing:

@misc{indic-mobile-2025,
  author    = {Rocky Singh Rajput},
  title     = {Indic-mobile: A 0.5B Language Model for All 22 Official Indian Languages},
  year      = {2025},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/RockySinghRajput/Indic-mobile}
}

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Model Card Author

Rocky Singh Rajput

For questions, feedback, or collaboration, please open a Community Discussion.