NLP Engineering Hub 📚

Enterprise NLP systems and LLM applications with distributed training support. Features custom language model implementations, efficient inference systems, and production-ready deployment pipelines.

Features • Installation • Quick Start • Documentation • Contributing

📑 Table of Contents

• Features
• Project Structure
• Prerequisites
• Installation
• Quick Start
• Documentation
  • Models
  • Pipeline Optimization
  • Benchmarks
• Contributing
• Versioning
• Authors
• Citation
• License
• Acknowledgments

✨ Features

• Custom LLM fine-tuning pipelines
• Multi-GPU distributed training
• Efficient inference optimization
• Production deployment patterns
• Memory-efficient implementations

📁 Project Structure

graph TD
    A[nlp-engineering-hub] --> B[models]
    A --> C[training]
    A --> D[inference]
    A --> E[deployment]
    B --> F[transformers]
    B --> G[embeddings]
    C --> H[distributed]
    C --> I[optimization]
    D --> J[serving]
    D --> K[scaling]
    E --> L[monitoring]
    E --> M[evaluation]

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Click to expand full directory structure

nlp-engineering-hub/
├── models/           # Model implementations
│   ├── transformers/ # Transformer architectures
│   └── embeddings/   # Embedding models
├── training/         # Training utilities
│   ├── distributed/  # Distributed training
│   └── optimization/ # Training optimizations
├── inference/        # Inference optimization
├── deployment/       # Deployment tools
├── tests/           # Unit tests
└── README.md        # Documentation

🔧 Prerequisites

• Python 3.8+
• CUDA 11.8+
• Transformers 4.35+
• PyTorch 2.2+
• NVIDIA GPU (16GB+ VRAM)

📦 Installation

# Clone repository
git clone https://github.com/BjornMelin/nlp-engineering-hub.git
cd nlp-engineering-hub

# Create environment
python -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

🚀 Quick Start

from nlp_hub import models, training

# Initialize model
model = models.TransformerWithQuantization(
    model_name="bert-base-uncased",
    quantization="int8"
)

# Configure distributed training
trainer = training.DistributedTrainer(
    model,
    num_gpus=4,
    mixed_precision=True
)

# Train efficiently
trainer.train(dataset, batch_size=32)

📚 Documentation

Models

Model	Task	Performance	Memory Usage
BERT-Optimized	Classification	92% accuracy	2GB
GPT-Efficient	Generation	85% ROUGE-L	4GB
T5-Distributed	Translation	42.5 BLEU	8GB

Pipeline Optimization

• Automatic mixed precision
• Dynamic batch sizing
• Gradient accumulation
• Model parallelism

Benchmarks

Performance on standard NLP tasks:

Task	Dataset	Model	GPUs	Training Time	Metric
Classification	GLUE	BERT	4xA100	2.5 hours	92% acc
Generation	CNN/DM	GPT	8xA100	8 hours	42.3 R1
QA	SQuAD	T5	2xA100	4 hours	88.5 F1

🤝 Contributing

• Contributing Guidelines
• Code of Conduct
• Development Guide

📌 Versioning

We use SemVer for versioning. For available versions, see the tags on this repository.

✍️ Authors

Bjorn Melin

• GitHub: @BjornMelin
• LinkedIn: Bjorn Melin

📝 Citation

@misc{melin2024nlpengineeringhub,
  author = {Melin, Bjorn},
  title = {NLP Engineering Hub: Enterprise Language Model Systems},
  year = {2024},
  publisher = {GitHub},
  url = {https://github.com/BjornMelin/nlp-engineering-hub}
}

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Hugging Face team
• LangChain developers
• PyTorch community

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Made with 📚 and ❤️ by Bjorn Melin