RockySinghRajput

Indic-mobile

README

License: apache-2.0

Model Details

Table with columns: Property, Value
Property	Value
Developed by	Rocky Singh Rajput
Model type	Causal Language Model
Architecture	Custom (from scratch)
Parameters	0.5B
Precision	BF16
Languages	All 22 official Indian languages
License	Apache 2.0
Trained from scratch	✅ Yes — not a fine-tune

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

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

Usage

Load with 🤗 Transformers

python
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

bash
ollama run hf.co/RockySinghRajput/Indic-mobile

Run with vLLM

bash
vllm serve RockySinghRajput/Indic-mobile

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

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

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.

Evaluation

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

Citation

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

bibtex
@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}
}

Model Card Author

Rocky Singh Rajput

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

Available on FriendliAI

Dedicated Endpoints

Run this model inference on single tenant GPU with unmatched speed and reliability at scale.

Learn more

Container

Run this model inference with full control and performance in your environment.

Learn more

Model Details

Model Provider

RockySinghRajput

Model Tree

Base

this model

Input Modalities

Text

Output Modalities

Text

Supported Functionality

Dedicated EndpointsContainer

Explore FriendliAI today

Get started Talk to an engineer

README

License: apache-2.0

Model Details

Table with columns: Property, Value
Property	Value
Developed by	Rocky Singh Rajput
Model type	Causal Language Model
Architecture	Custom (from scratch)
Parameters	0.5B
Precision	BF16
Languages	All 22 official Indian languages
License	Apache 2.0
Trained from scratch	✅ Yes — not a fine-tune

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

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

Usage

Load with 🤗 Transformers

python
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

bash
ollama run hf.co/RockySinghRajput/Indic-mobile

Run with vLLM

bash
vllm serve RockySinghRajput/Indic-mobile

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

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

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.

Evaluation

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

Citation

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

bibtex
@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}
}

Model Card Author

Rocky Singh Rajput

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