Nyuntam 🚀

Nyuntam is NyunAI's cutting-edge toolkit for optimizing and accelerating large language models (LLMs) through state-of-the-art compression techniques. 🛠️ With an integrated CLI, managing your workflows and experimenting with various compression methods has never been easier! ✨

Quick Start ⚡

Ready to dive in? Here's a minimal example to get you up and running with Nyuntam:

1. Initialize Your Workspace: 🗂️ First, set up your workspace using the nyun init command. This creates the necessary directories and configurations for your experiments.

   nyun init ~/my-workspace ~/my-data --extensions text-gen
   

   This command initializes a workspace at ~/my-workspace, sets the custom data path to ~/my-data, and installs the text-gen extension.

2. Run an Example Experiment: 🏃‍♀️ Now, run an example experiment using a pre-configured YAML file. For instance, to try out FLAP pruning:

   nyun run examples/text-generation/flap_pruning/config.yaml
   

   This command executes the main script using the configurations specified in the provided YAML file.

Key Features ✨

• State-of-the-Art Compression: 🗜️ Includes advanced techniques like pruning, quantization, and distillation to ensure model efficiency without sacrificing performance.
• Multi-Platform Support: 💻 Run experiments seamlessly on various platforms using Docker or virtual environments.
• Integrated CLI: ⌨️ Built-in command-line interface (nyun) for easy workspace management and experiment execution.
• Extensible Architecture: 🧩 Supports various SOTA compression algorithms, using a single cli command.

Installation 🛠️

Prerequisites

• Python 3.8 or later
• For GPU support: NVIDIA Container Toolkit (when using Docker) 🐳

Quick Install

Install Nyuntam using pip:

pip install nyuntam

Alternative Installation Methods

Git + Docker 🐳

1. Install NVIDIA Container Toolkit (Linux):

   curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
   
   curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
     sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
     sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
   
   sudo apt-get update
   sudo apt-get install -y nvidia-container-toolkit
   sudo nvidia-ctk runtime configure --runtime=docker
   sudo systemctl restart docker
   

2. Clone and Setup:

   git clone --recursive https://github.com/nyunAI/nyuntam.git
   cd nyuntam
   docker pull nyunadmin/nyuntam-text-generation:latest
   docker run -it -d --gpus all -v $(pwd):/workspace --name nyuntam-dev --network=host nyunadmin/nyuntam-text-generation:latest bash
   

Git + Virtual Environment 🧪

1. Clone Repository:

   git clone --recursive https://github.com/nyunAI/nyuntam.git
   cd nyuntam
   

2. Setup Environment:

   python3 -m venv {ENVIRONMENT_NAME}
   source {ENVIRONMENT_NAME}/bin/activate
   pip install -r requirements.txt
   

Using Setup.py

Development 🧑‍💻

This section is for developers who want to dive deep and modify the Nyuntam codebase.

Setting Up Your Development Environment

1. Clone the Repository:

   git clone --recursive https://github.com/nyunAI/nyuntam.git
   cd nyuntam
   

2. Choose Your Environment:

   • Docker: Follow the instructions in the "Git + Docker" section above.
   • Virtual Environment: Follow the instructions in the "Git + Virtual Environment" section above.

Working with the Code

• Core Scripts: The core scripts like main.py, algorithm.py, and commands.py are located in the root directory of the nyuntam folder.
• Examples: Practical examples for different tasks are located in the nyuntam/examples directory. Each subdirectory includes a README.md for guidance and config.yaml files for configurations.
• Modules: The main modules for text generation are located in the nyuntam/text_generation directory.
• Utilities: Utility scripts and functions are located in the nyuntam/utils directory.

Running Experiments (Development)

1. Prepare Configuration: Create a YAML file defining your experiment parameters. Example configurations are available in the nyuntam/examples directory.

   • Refer to dataset imports and models imports for configuration details.
   • Scripts and example YAML files are available here.

2. Execute:

   python nyuntam/main.py --yaml_path path/to/recipe.yaml
   

Usage ⚙️

Initializing a Workspace

Before running experiments, initialize your workspace:

nyun init [WORKSPACE_PATH] [CUSTOM_DATA_PATH] [OPTIONS]

Options:

• --overwrite, -o: Overwrite existing workspace
• --extensions, -e: Specify extensions to install:
  • text-gen: For text generation
  • all: Install all extensions
  • none: No extensions

Example:

nyun init ~/my-workspace ~/my-data --extensions text-gen

Running Experiments

1. Prepare Configuration: Create a YAML file defining your experiment parameters. Example configurations are available in the nyuntam/examples directory.

   • Refer to dataset imports and models imports for configuration details.
   • Scripts and example YAML files are available here.

2. Execute:

   nyun run path/to/recipe.yaml
   

   For chained execution:

   nyun run script1.yaml script2.yaml
   

Examples 💡

For detailed examples and use cases, check out our examples directory, which includes:

Text Generation

• Maximising math performance for extreme compressions: 2-bit Llama3-8b (w2a16)
• Efficient 4-bit Quantization (w4a16) of Llama3.1-8b
• Llama3.1 70B: 0.5x the cost & size
• Achieving Up to 2.5x TensorRTLLM Speedups
• Accelerating a 4-bit Quantised Llama Model

Documentation 📚

For complete documentation, visit NyunAI Docs

Version Information ℹ️

Check your installed version:

nyun version

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NOTE: For access to gated repositories within containers, ensure you have the necessary Hugging Face tokens configured. 🔑