zenith-nlp-framework 1.0.0

An advanced, from-scratch NLP framework for training and deploying modern transformer models.

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Zenith NLP Framework

A Framework for Advanced Natural Language Processing

ZenithNLP is an advanced, from-scratch NLP framework built with PyTorch for training, fine-tuning, and deploying modern transformer-based models. It serves as a comprehensive toolkit for NLP practitioners and researchers, featuring a modular architecture and a full suite of MLOps capabilities.

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📜 Table of Contents

• ✨ Features
• 🚀 Getting Started
• 📖 Tutorial: Training a Text Classifier
• 🏛️ Framework Architecture
• 🤝 Contributing
• 📄 License

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✨ Features

• State-of-the-Art Model Architectures: From-scratch implementations of:
  • BERT (Encoder-only) for tasks like classification and NER.
  • GPT (Decoder-only) for causal language modeling and text generation.
  • Seq2SeqTransformer (Encoder-Decoder) for translation and summarization.
• Advanced Training Techniques:
  • Parameter-Efficient Fine-Tuning (PEFT): Integrated LoRA (Low-Rank Adaptation) for efficient fine-tuning of large models.
  • Distributed Training: Support for multi-GPU training using PyTorch's DistributedDataParallel.
  • Advanced Optimization: Includes learning rate scheduling with warm-up and gradient clipping.
• Full MLOps Pipeline:
  • Configuration Management: Powered by Hydra, allowing for flexible and reproducible experiments through YAML files.
  • Experiment Tracking: Integrated with MLflow to log parameters, metrics, and model artifacts automatically.
  • Containerization: Fully containerized with Docker and Docker Compose for reproducible environments and easy deployment of the MLflow UI.
  • Continuous Integration: Automated testing pipeline with GitHub Actions and pytest.
• Flexible API for Deployment:
  • A ready-to-use FastAPI server that can dynamically load and serve any model trained with the framework.
• Custom Core Components:
  • A trainable Byte-Pair Encoding (BPE) Tokenizer built from scratch.
  • Modular implementations of MultiHeadAttention, PositionalEncoding, and other core transformer building blocks.

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🚀 Getting Started

1. Installation (from PyPI)

Note: Once published, you will be able to install the framework directly from PyPI.

pip install zenith-nlp-framework

2. Local Development Setup

# 1. Clone the repository
git clone https://github.com/cattolatte/zenith-nlp-framework.git
cd zenith-nlp-framework

# 2. Create and activate a virtual environment
python3 -m venv venv
source venv/bin/activate

# 3. Install all dependencies
pip install -r requirements.txt

# 4. Install the project in editable mode
pip install -e .

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📖 Tutorial: Training a Text Classifier

This framework is designed for flexibility. Here’s how you can train your own text classification model.

1. Prepare Your Data and Configs

Place your training data (e.g., my_data.csv) in a local data/ directory. Use the configs/ directory as a template. You can modify config.yaml or create a new one to point to your data file and adjust model/training parameters.

2. Run Training

Run the text classification task script. All parameters are managed by the Hydra configuration files in the configs/ directory.

# Run with default settings from the config files
python3 -m my_nlp_framework.tasks.text_classification

You can easily override any parameter from the command line:

# Train for more epochs with a different learning rate
python3 -m my_nlp_framework.tasks.text_classification training.epochs=10 training.learning_rate=0.0005

# Train with LoRA enabled
python3 -m my_nlp_framework.tasks.text_classification model.use_lora=True model.lora_rank=8

3. Track Experiments with MLflow

Before training, launch the MLflow UI to track your experiments in real-time. The docker-compose.yml file is pre-configured for you.

# Start the MLflow server in the background
docker-compose up -d

Navigate to http://localhost:5000 in your browser to view the MLflow dashboard.

🌐 Serving Your Model via API

Once you have a trained model (.pth file) and tokenizer (.json file), you can easily deploy it with the built-in FastAPI server.

python3 -m my_nlp_framework.inference.api \
    --model-path /path/to/your/trained_model.pth \
    --tokenizer-path /path/to/your/tokenizer.json \
    --vocab-size 10000 \
    --num-classes 2

The API will be available at http://localhost:8000/docs for interactive testing.

🐳 Running with Docker

You can also run the entire training process within a Docker container for perfect reproducibility.

# 1. Build the Docker image
docker build -t zenith-nlp-framework:latest .

# 2. Run a task (mounting your local data directory)
docker run --rm -v "$(pwd)/data":/app/data zenith-nlp-framework:latest \
  python -m my_nlp_framework.tasks.text_classification

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🏛️ Framework Architecture

This framework is organized into several key modules:

• src/my_nlp_framework/core: Contains the fundamental building blocks like attention mechanisms, LoRA layers, and tokenizers.
• src/my_nlp_framework/models: Defines high-level model architectures like BERT and GPT.
• src/my_nlp_framework/data: Includes flexible data loaders.
• src/my_nlp_framework/training: A powerful, centralized training engine with advanced features.
• src/my_nlp_framework/tasks: Example scripts that show how to use the framework to solve end-to-end problems.
• src/my_nlp_framework/inference: Code for deploying and serving trained models.
• configs/: Centralized YAML configuration files for Hydra.
• tests/: Unit and integration tests for the framework.

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🤝 Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue.

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📄 License

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

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Made with ❤️ by K Satya Sai Nischal