brahmgen 0.1.0
Enterprise Model Training, Evaluation & Governance Platform
Requires Python: >=3.9

# BrahmGen: Foundational Model Development Platform

> **Build. Train. Deploy. Scale.**

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BrahmGen is a comprehensive platform for developing, training, and deploying large-scale foundational AI models, from 1B to 1T+ parameters, with distributed training, multi-modal capabilities, and production-ready inference optimization.

## 🚀 Key Features

### **Extreme-Scale Training**
- **Distributed Training**: 3D parallelism (Data, Model, Pipeline) across 1000+ GPUs
- **Memory Optimization**: ZeRO-3, activation checkpointing, and gradient accumulation
- **Mixed Precision**: FP16/BF16/FP8 training with automatic loss scaling
- **Flash Attention**: Memory-efficient attention for sequences up to 128K tokens

### **Multi-Modal Architecture**
- **Unified Framework**: Text, code, images, audio, and video in a single model
- **Cross-Modal Fusion**: Advanced attention mechanisms for modal interaction
- **Modular Design**: Plug-and-play encoders/decoders for different modalities
- **Dynamic Routing**: Adaptive computation paths based on input modality

### **Production Deployment**
- **Inference Optimization**: Quantization (INT8/INT4), pruning, and distillation
- **Serving Infrastructure**: High-throughput inference with batching and caching
- **Edge Deployment**: Model compression for mobile and embedded devices
- **Auto-Scaling**: Kubernetes-native deployment with horizontal pod autoscaling

### **Model Architecture Zoo**
- **Transformers**: GPT, BERT, T5, and custom architectures
- **Mixture of Experts**: Sparse models with dynamic expert routing
- **Diffusion Models**: Text-to-image, image-to-image generation
- **Neural Operators**: Physics-informed architectures for scientific computing

### **Data Engineering**
- **Petabyte-Scale Processing**: Distributed data pipelines with Apache Spark
- **Quality Filtering**: Deduplication, toxicity detection, and format validation
- **Tokenization**: BPE, SentencePiece, and custom tokenizers
- **Synthetic Data**: LLM-based data augmentation and generation

## 🏗️ Architecture Overview

```
BrahmGen::
├── Model Development
│   ├── Architectures (Transformer, MoE, Diffusion, Neural Operators)
│   ├── Layers (Attention, FFN, Normalization, Embeddings)
│   ├── Optimizers (AdamW, LAMB, Lion, Sophia)
│   └── Loss Functions (Cross-Entropy, Contrastive, RLHF)
├── Training Infrastructure
│   ├── Distributed Training (DDP, FSDP, DeepSpeed)
│   ├── Data Loading (Streaming, Sharding, Prefetching)
│   ├── Checkpointing (Async, Incremental, Cloud Storage)
│   └── Monitoring (Tensorboard, W&B, MLflow)
├── Inference Engine
│   ├── Optimization (Quantization, Pruning, Knowledge Distillation)
│   ├── Serving (TorchServe, Triton, vLLM)
│   ├── Caching (KV-cache, Prompt Cache)
│   └── Batching (Dynamic, Continuous, Priority-based)
└── Platform Integration
    ├── BrahmAI Platform API
    ├── ScireX Training Clusters
    ├── BrahmDAX Data Pipelines
    └── Model Garden Registry
```

### Core Design Principles

1. **Scale-First Design**: Built for trillion-parameter models from day one
2. **Hardware Agnostic**: Supports NVIDIA, AMD, Intel, TPU, and custom ASICs
3. **Production Ready**: Enterprise security, compliance, and monitoring built-in
4. **Research Friendly**: Rapid experimentation with modular components
5. **Cost Optimized**: Spot instance support, gradient checkpointing, and efficient scheduling

## 🚦 Quick Start

### Prerequisites

```bash
# Minimum Requirements
- Python 3.10+
- CUDA 12.0+ (for GPU support)
- 32GB+ RAM (128GB+ recommended)
- 100GB+ storage for models
- Docker 20.10+ (optional, for containerized deployment)
```

### Installation

```bash
# Clone the repository
git clone https://github.com/zenteiq/brahmgen.git
cd brahmgen

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install BrahmGen
pip install -e ".[all]"

# Verify installation
brahmgen --version
```

### Basic Usage

```python
from brahmgen import FoundationalModel, Trainer, DataLoader
from brahmgen.architectures import GPTConfig

# Configure model architecture
config = GPTConfig(
    vocab_size=50257,
    n_positions=2048,
    n_embd=2048,
    n_layer=24,
    n_head=32,
    activation="swiglu",
    use_flash_attention=True
)

# Initialize model
model = FoundationalModel(config)

# Setup distributed training
trainer = Trainer(
    model=model,
    precision="bf16-mixed",
    gradient_checkpointing=True,
    distributed_backend="fsdp",
    num_gpus=8
)

# Load dataset
dataloader = DataLoader(
    dataset_path="s3://brahmgen-data/pretrain",
    batch_size=2048,
    sequence_length=2048,
    num_workers=32
)

# Start training
trainer.fit(model, dataloader, epochs=10)
```

## 📊 Performance Benchmarks

### Training Performance
- **GPT-175B**: 40 days on 512 A100 GPUs (vs 90 days baseline)
- **Throughput**: 450 TFLOPS/GPU sustained (90% theoretical peak)
- **Scaling Efficiency**: 95% weak scaling up to 1024 GPUs
- **Memory Efficiency**: 3x reduction with ZeRO-3 + activation checkpointing

### Inference Performance
- **Latency**: <100ms for 1B parameters, <500ms for 70B parameters
- **Throughput**: 10,000+ tokens/second with continuous batching
- **Quantization**: 4x speedup with INT8, 8x with INT4 (< 1% accuracy loss)
- **Edge Deployment**: 30B model on NVIDIA Orin (15 tokens/sec)

### Model Quality
- **MMLU Benchmark**: State-of-the-art across all model sizes
- **HumanEval**: 85%+ pass@1 for code generation
- **Multi-Modal**: COCO, ImageNet, AudioSet SOTA results
- **Scientific**: 95%+ accuracy on physics/chemistry benchmarks

## 🎯 Use Cases

### **Enterprise AI**
- Custom LLMs for domain-specific tasks
- Multi-lingual customer support
- Code generation and review
- Document understanding and generation

### **Scientific Computing**
- Protein folding prediction
- Climate modeling acceleration
- Drug discovery and molecular design
- Physics simulation surrogates

### **Creative AI**
- Text-to-image generation
- Music composition
- Video generation and editing
- 3D content creation

### **Edge Intelligence**
- On-device language models
- Real-time translation
- Privacy-preserving inference
- Autonomous vehicle perception

## 🔧 Advanced Features

### 3D Parallel Training

```python
from brahmgen.distributed import ParallelConfig

# Configure 3D parallelism
parallel_config = ParallelConfig(
    tensor_parallel_size=4,      # Split model across 4 GPUs
    pipeline_parallel_size=2,    # 2-stage pipeline
    data_parallel_size=32,       # 32-way data parallelism
    sequence_parallel=True,      # Sequence parallelism for long contexts
    zero_stage=3                 # ZeRO-3 optimization
)

trainer = Trainer(
    model=model,
    parallel_config=parallel_config,
    gradient_accumulation_steps=16,
    compile_model=True  # torch.compile for 30% speedup
)
```

### Mixture of Experts

```python
from brahmgen.architectures import MoEConfig

# Configure sparse MoE model
moe_config = MoEConfig(
    num_experts=128,
    expert_capacity=2.0,
    top_k=2,  # Route to top-2 experts
    load_balancing_loss_weight=0.01,
    router_type="switch"  # Switch Transformer routing
)

model = FoundationalModel(
    base_config=config,
    moe_config=moe_config
)
```

### RLHF Fine-tuning

```python
from brahmgen.rlhf import RLHFTrainer, RewardModel

# Load pre-trained model and reward model
base_model = FoundationalModel.from_pretrained("brahmgen-70b")
reward_model = RewardModel.from_pretrained("brahmgen-rm-7b")

# Configure RLHF training
rlhf_trainer = RLHFTrainer(
    model=base_model,
    reward_model=reward_model,
    ppo_epochs=4,
    learning_rate=1e-5,
    kl_penalty=0.1
)

# Fine-tune with human feedback
rlhf_trainer.train(
    dataset="human_preferences_v2",
    num_epochs=3
)
```

### Production Deployment

```python
from brahmgen.serving import ModelServer
from brahmgen.optimization import quantize_model

# Optimize model for inference
optimized_model = quantize_model(
    model,
    quantization="int8-dynamic",
    calibration_data=calibration_loader
)

# Deploy with auto-scaling
server = ModelServer(
    model=optimized_model,
    engine="vllm",  # High-performance inference engine
    max_batch_size=256,
    enable_continuous_batching=True,
    cache_size="100GB"
)

# Start serving
server.deploy(
    replicas=4,
    gpu_per_replica=1,
    autoscale_config={
        "min_replicas": 2,
        "max_replicas": 16,
        "target_gpu_utilization": 0.8
    }
)
```

## 📁 Project Structure

```
brahmgen/
├── brahmgen/                       # Core library
│   ├── architectures/              # Model architectures
│   │   ├── transformers/          # GPT, BERT, T5, etc.
│   │   ├── moe/                   # Mixture of Experts
│   │   ├── diffusion/             # Diffusion models
│   │   └── multimodal/            # Multi-modal architectures
│   ├── training/                   # Training infrastructure
│   │   ├── distributed/           # Distributed training
│   │   ├── optimizers/            # Custom optimizers
│   │   └── schedulers/            # Learning rate schedulers
│   ├── data/                      # Data processing
│   │   ├── datasets/              # Dataset implementations
│   │   ├── tokenizers/            # Tokenization
│   │   └── preprocessing/         # Data cleaning
│   ├── inference/                  # Inference optimization
│   │   ├── quantization/          # Model quantization
│   │   ├── pruning/               # Weight pruning
│   │   └── serving/               # Model serving
│   └── evaluation/                 # Benchmarking
│       ├── metrics/               # Evaluation metrics
│       ├── benchmarks/            # Standard benchmarks
│       └── analysis/              # Model analysis tools
├── configs/                        # Configuration files
├── scripts/                        # Training/inference scripts
├── tests/                         # Unit and integration tests
├── benchmarks/                    # Performance benchmarks
├── examples/                      # Usage examples
└── docs/                         # Documentation
```

## 🧪 Testing & Validation

### Model Testing
```bash
# Run comprehensive model tests
pytest tests/models --cov=brahmgen.architectures

# Test distributed training
mpirun -n 8 python tests/distributed/test_3d_parallel.py

# Benchmark inference performance
python benchmarks/inference_benchmark.py --model brahmgen-7b --batch-size 32
```

### Quality Validation
```bash
# Evaluate on standard benchmarks
brahmgen evaluate --model checkpoint/iter_10000 --benchmarks mmlu,humaneval,coco

# Analyze model behavior
brahmgen analyze --model brahmgen-70b --dataset validation --metrics perplexity,attention_patterns
```

### Production Testing
```bash
# Load testing for serving
locust -f tests/load_test.py --host http://localhost:8000 --users 1000

# End-to-end integration test
python tests/e2e/test_full_pipeline.py --from-training --to-deployment
```

## 📈 Roadmap

### Version 0.5 (Q1 2025)
- [x] **Core Architecture**: Transformer, MoE implementations
- [x] **Basic Training**: Single-node multi-GPU training
- [x] **Data Pipeline**: Basic preprocessing and tokenization
- [ ] **Documentation**: Comprehensive API documentation

### Version 1.0 (Q2 2025)
- [ ] **Distributed Training**: Full 3D parallelism support
- [ ] **Multi-Modal**: Unified text-image-audio models
- [ ] **RLHF**: Complete reinforcement learning pipeline
- [ ] **Production Serving**: Enterprise-ready deployment

### Version 2.0 (Q3 2025)
- [ ] **Trillion Parameters**: Support for 1T+ parameter models
- [ ] **Custom Hardware**: TPU v5, Gaudi2, custom ASIC support
- [ ] **AutoML**: Neural architecture search for foundational models
- [ ] **Federation**: Federated learning across organizations

### Version 3.0 (Q4 2025)
- [ ] **Continual Learning**: Online learning from production data
- [ ] **Embodied AI**: Integration with robotics platforms
- [ ] **Quantum Integration**: Hybrid classical-quantum models
- [ ] **AGI Features**: Advanced reasoning and planning capabilities

## 🤝 Contributing

BrahmGen is currently a proprietary platform developed by Zenteiq Aitech Innovations. For collaboration opportunities, licensing, or custom model development:

**Contact**: brahmgen@zenteiq.ai  
**Enterprise**: enterprise@zenteiq.ai  
**Website**: https://brahmgen.zenteiq.ai

## 📝 License

Copyright (c) 2025 Zenteiq Aitech Innovations Private Limited. All rights reserved.

BrahmGen is proprietary software. Distribution, reproduction, or use without express written authorization from Zenteiq is strictly prohibited.

For licensing inquiries: licensing@zenteiq.ai

## 🏆 Acknowledgments

- **Prof. Dr. Sashikumaar Ganesan** - Chief AI Architect
- **Zenteiq Aitech Innovations** - Development and Infrastructure
- **IISc Bangalore** - Research Collaboration
- **NVIDIA** - GPU Computing Partnership
- **Microsoft Azure** - Cloud Infrastructure
- **Hugging Face** - Model Hub Integration

## 🌟 Citation

If you use BrahmGen in your research, please cite:

```bibtex
@software{brahmgen2025,
  title = {BrahmGen: Foundational Model Development Platform},
  author = {Zenteiq Aitech Innovations},
  year = {2025},
  url = {https://brahmgen.zenteiq.ai}
}
```

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