Instructions to use RockySinghRajput/Indic-mobile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RockySinghRajput/Indic-mobile with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RockySinghRajput/Indic-mobile")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RockySinghRajput/Indic-mobile")
model = AutoModelForCausalLM.from_pretrained("RockySinghRajput/Indic-mobile")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RockySinghRajput/Indic-mobile with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RockySinghRajput/Indic-mobile"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RockySinghRajput/Indic-mobile",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RockySinghRajput/Indic-mobile

SGLang

How to use RockySinghRajput/Indic-mobile with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RockySinghRajput/Indic-mobile" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RockySinghRajput/Indic-mobile",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "RockySinghRajput/Indic-mobile" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RockySinghRajput/Indic-mobile",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RockySinghRajput/Indic-mobile with Docker Model Runner:
```
docker model run hf.co/RockySinghRajput/Indic-mobile
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

Model Details

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

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

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:

@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.

Downloads last month: 66

Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for RockySinghRajput/Indic-mobile

Quantizations

1 model