Strategic Partnership Program for India's AI Sovereignty

Empowering India's AI Future Through Collaboration

The Bharat Foundation Model Framework (BFMF) represents a landmark initiative in India's journey towards AI independence. As a strategic, community-driven open-source project, we are developing sovereign foundation models that are uniquely Indian - trained on our data, built with cutting-edge open technologies, and deployed on domestic infrastructure.

To accelerate this national mission, we invite leading institutions, technology companies, research organizations, and visionary individuals to join us as strategic partners in building India's AI future.

Partnership Opportunities

Partnership Benefits

Strategic Advantages

• Priority Access: Early preview and integration rights for new Bharat models
• Co-Innovation: Joint R&D opportunities and technology collaboration
• Market Leadership: First-mover advantage in India's emerging AI ecosystem

Institutional Benefits

• Brand Recognition: Featured placement on BFMF's partner ecosystem
• Technical Support: Dedicated integration and optimization assistance
• Research Publications: Co-authored papers and technical documentation

Current Strategic Requirements (Q4 2025)

Become a Strategic Partner

Join us in shaping India's AI future. Connect with our partnership team:

• Email: partnerships@bharat-ai.org
• Strategic Discussions: Partner Inquiry Portal
• Partnership Portal: https://bharat-ai.org/partners (launching soon)

Our team will provide detailed information about partnership opportunities, technical requirements, and engagement frameworks.

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"Every partnership strengthens India's path to AI sovereignty. Together, we're not just adopting AI - we're defining its future with Indian innovation and values."

Join us in building India's sovereign AI capabilities. The future of AI is being written in Bharat.

🇮🇳 Bharat Foundation Model Framework (BFMF)

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🧭 Vision

To empower India's AI independence by providing an open, modular, and scalable foundation model framework — enabling developers, researchers, and institutions to build Indian-language, domain-specific, and locally governed AI systems.

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⚙️ Core Philosophy

• 🇮🇳 Built for Bharat – Native support for Indian languages, datasets, and regional diversity.
• 🧠 Foundation First – Focused on base model pretraining, fine-tuning, and adaptation.
• 🔒 Sovereign AI – 100% self-hostable, privacy-first, and data-residency compliant.
• 🌍 Open Collaboration – Interoperable with Hugging Face, OpenAI-style APIs, and ExoStack.
• 🧩 Modular Stack – Each layer can work independently or as part of a complete pipeline.

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🏗️ High-Level Architecture

 ┌───────────────────────────────────────────────┐
 │                BharatFM Stack                 │
 ├───────────────────────────────────────────────┤
 │ 7. Governance & Registry (MLflow, Audit, ACL) │
 │ 6. Serving & Deployment (vLLM, ExoStack)      │
 │ 5. Evaluation & Benchmark (HELM, lm-eval)     │
 │ 4. Fine-tuning Interface (Axolotl, LoRA)      │
 │ 3. Training Engine (Deepspeed, Megatron)      │
 │ 2. Model Architectures (GLM, LLaMA, Mistral)  │
 │ 1. Data Layer (Indic Corpora, RedPajama)      │
 └───────────────────────────────────────────────┘

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🧩 Modular Components

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🧠 Supported Model Families

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🌐 Integration with Other Frameworks

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🚀 Quick Start

Prerequisites

• Python 3.8+
• PyTorch 2.0+
• CUDA 11.8+ (for GPU training)
• 16GB+ RAM (for 1.3B model training)

Installation

# Clone the repository
git clone https://github.com/bharat-ai/bharat-fm.git
cd bharat-fm

# Install in development mode
pip install -e ".[dev]"

# Or install with specific extras
pip install -e ".[train,eval,deploy]"

Basic Usage

1. Train Bharat-Lite (1.3B) for Hindi-English Chat

bharat train --model glm --dataset indic_mix --steps 50000

2. Fine-tune Bharat-Gov for Policy AI

bharat finetune --model bharat-base --lora --dataset govt_data

3. Deploy via ExoStack

bharat deploy --model bharat-gov --infra exostack --replicas 3

4. Evaluate Model Performance

bharat eval --model bharat-base --benchmark helm --languages hi,en

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📁 Project Structure

bharat-fm/
│
├── README.md
├── LICENSE
├── pyproject.toml
├── requirements.txt
│
├── bharat_data/
│   ├── datasets/
│   ├── tokenizers/
│   └── preprocess.py
│
├── bharat_model/
│   ├── config/
│   ├── modeling_glm.py
│   ├── modeling_llama.py
│   └── modeling_moe.py
│
├── bharat_train/
│   ├── trainer.py
│   ├── finetune.py
│   └── deepspeed_config.json
│
├── bharat_eval/
│   ├── benchmarks/
│   └── evaluator.py
│
├── bharat_deploy/
│   ├── api.py
│   └── inference_server.py
│
├── bharat_registry/
│   └── mlflow_utils.py
│
├── bharat_cli/
│   └── main.py
│
├── docs/
│   ├── architecture.png
│   └── user_guide.md
│
├── examples/
│   ├── hello_bharat.py
│   └── config_examples/
│
└── tests/
    ├── test_data.py
    ├── test_model.py
    └── test_train.py

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📘 Example Use Cases

1. Multilingual Chatbot

from bharat_model import BharatLite
from bharat_deploy import InferenceServer

# Load pre-trained model
model = BharatLite.from_pretrained("bharat-ai/bharat-lite-1.3b")

# Create inference server
server = InferenceServer(model, host="0.0.0.0", port=8000)
server.start()

2. Custom Fine-tuning

from bharat_train import FineTuner
from bharat_data import IndicDataset

# Load dataset
dataset = IndicDataset("hindi_english_pairs")

# Initialize fine-tuner
finetuner = FineTuner(
    base_model="bharat-base",
    lora_rank=16,
    learning_rate=2e-5
)

# Fine-tune
finetuner.train(dataset, epochs=3)

3. Model Evaluation

from bharat_eval import Evaluator
from bharat_model import BharatBase

# Load model
model = BharatBase.from_pretrained("bharat-base")

# Initialize evaluator
evaluator = Evaluator(model)

# Run benchmarks
results = evaluator.evaluate(
    benchmarks=["helm", "lm-eval"],
    languages=["hi", "en", "bn"]
)

print(results)

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🛠️ Development

Setting up Development Environment

# Clone and install
git clone https://github.com/bharat-ai/bharat-fm.git
cd bharat-fm
pip install -e ".[dev]"

# Install pre-commit hooks
pre-commit install

# Run tests
pytest tests/

# Run linting
ruff check bharat_/
black bharat_/

Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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🚀 Roadmap (Phase-wise)

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🏛️ Governance

Steering Committee

• Technical Lead: AI Research Institute, India
• Community Lead: Open Source India Foundation
• Industry Lead: Bharat AI Consortium

Decision Making Process

• RFC Process
• Community Meetings
• Technical Advisory Board

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📄 License

This project is licensed under the Apache 2.0 / Bharat Open AI License (BOAL).

• ✅ Commercial usage allowed
• ✅ Academic usage allowed
• ✅ Modification and distribution allowed
• 📝 India-first attribution required

See LICENSE for the full license text.

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🤝 Community & Support

• GitHub Discussions: Join the conversation
• Discord Community: Join our Discord
• Documentation: Read the docs
• Issues: Report bugs or request features

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🙏 Acknowledgments

• Government of India - For supporting sovereign AI initiatives
• Indian AI Research Community - For technical guidance and expertise
• Open Source Community - For building the foundation we build upon
• Hugging Face - For the amazing transformers ecosystem
• ExoStack - For compute infrastructure integration

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📈 Citation

If you use BFMF in your research, please cite:

@software{bharat_fmf_2025,
  title={Bharat Foundation Model Framework: India's Open-Source Ecosystem for Sovereign AI},
  author={Bharat AI Team},
  year={2025},
  url={https://github.com/bharat-ai/bharat-fm},
  license={Apache 2.0 / BOAL}
}

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