https://github.com/bjornmelin/nlp-engineering-hub
📚 Enterprise NLP systems and LLM applications. Features custom language model implementations, distributed training pipelines, and efficient inference systems. 🔤
https://github.com/bjornmelin/nlp-engineering-hub
cuda gpu-optimization huggingface huggingface-transformers langchain language-models large-language-models nlp openai python transformers
Last synced: 3 months ago
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📚 Enterprise NLP systems and LLM applications. Features custom language model implementations, distributed training pipelines, and efficient inference systems. 🔤
- Host: GitHub
- URL: https://github.com/bjornmelin/nlp-engineering-hub
- Owner: BjornMelin
- License: mit
- Created: 2025-01-24T14:39:36.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-01-24T14:41:44.000Z (5 months ago)
- Last Synced: 2025-01-24T15:31:10.723Z (5 months ago)
- Topics: cuda, gpu-optimization, huggingface, huggingface-transformers, langchain, language-models, large-language-models, nlp, openai, python, transformers
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# NLP Engineering Hub 📚
[](https://www.python.org/downloads/)
[](https://huggingface.co/docs/transformers/index)
[](https://langchain.org)
[](https://developer.nvidia.com/cuda-toolkit)
[](LICENSE)
> Enterprise NLP systems and LLM applications with distributed training support. Features custom language model implementations, efficient inference systems, and production-ready deployment pipelines.
[Features](#features) • [Installation](#installation) • [Quick Start](#quick-start) • [Documentation](#documentation) • [Contributing](#contributing)
## 📑 Table of Contents
- [Features](#features)
- [Project Structure](#project-structure)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Quick Start](#quick-start)
- [Documentation](#documentation)
- [Models](#models)
- [Pipeline Optimization](#pipeline-optimization)
- [Benchmarks](#benchmarks)
- [Contributing](#contributing)
- [Versioning](#versioning)
- [Authors](#authors)
- [Citation](#citation)
- [License](#license)
- [Acknowledgments](#acknowledgments)
## ✨ Features
- Custom LLM fine-tuning pipelines
- Multi-GPU distributed training
- Efficient inference optimization
- Production deployment patterns
- Memory-efficient implementations
## 📁 Project Structure
```mermaid
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]
```
Click to expand full directory structure
```plaintext
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
```bash
# 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
```python
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](CONTRIBUTING.md)
- [Code of Conduct](CODE_OF_CONDUCT.md)
- [Development Guide](DEVELOPMENT.md)
## 📌 Versioning
We use [SemVer](http://semver.org/) for versioning. For available versions, see the [tags on this repository](https://github.com/BjornMelin/nlp-engineering-hub/tags).
## ✍️ Authors
**Bjorn Melin**
- GitHub: [@BjornMelin](https://github.com/BjornMelin)
- LinkedIn: [Bjorn Melin](https://linkedin.com/in/bjorn-melin)
## 📝 Citation
```bibtex
@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](LICENSE) file for details.
## 🙏 Acknowledgments
- Hugging Face team
- LangChain developers
- PyTorch community
---
Made with 📚 and ❤️ by Bjorn Melin