Gemma 2-2b-it CPU Inference in Pure C#

Welcome to the Gemma 2-2b-it repository! This project implements int8 CPU inference in a single file of pure C#. It serves as a robust solution for those interested in leveraging low-level machine learning models efficiently on CPU architectures.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Technical Details
• Contributing
• License
• Links

Introduction

Gemma 2-2b-it is designed for developers and researchers who need an efficient inference engine for large language models (LLMs). The focus is on performance and ease of use, allowing you to implement and run your models without the overhead of complex libraries. This project is particularly useful for those working in environments with limited resources.

Features

• Int8 Inference: Optimize your models for speed and efficiency using int8 quantization.
• Single File Implementation: Simple deployment with everything in one C# file.
• CPU Optimization: Tailored for CPU architectures, ensuring fast execution without the need for GPUs.
• Easy Integration: Seamlessly integrate with existing C# applications.
• Comprehensive Documentation: Detailed instructions to help you get started quickly.

Installation

To get started with Gemma 2-2b-it, follow these steps:

1. Download the latest release from the Releases section.
2. Extract the files to your desired directory.
3. Open the C# file in your preferred IDE or text editor.

Usage

After setting up the repository, you can run the inference engine by following these steps:

1. Load your model: Ensure your model is compatible with int8 quantization.
2. Call the inference method: Use the provided functions to run inference on your input data.
3. Retrieve the results: Access the output directly from the inference call.

Here is a simple example of how to use the library:

using Gemma;

class Program
{
    static void Main(string[] args)
    {
        var model = new Model("path/to/your/model");
        var input = new InputData(...);
        var output = model.Infer(input);
        
        Console.WriteLine("Inference Result: " + output);
    }
}

For more detailed usage examples, please refer to the documentation provided in the repository.

Technical Details

Architecture

Gemma 2-2b-it uses a straightforward architecture that emphasizes performance. The inference engine is built to handle int8 data efficiently, minimizing the overhead commonly associated with model execution. The architecture supports:

• Quantization: Transforming models to int8 format to reduce memory usage and improve speed.
• Low-level Optimization: Leveraging CPU capabilities to enhance performance without the need for complex frameworks.

Performance Metrics

Performance is critical in inference engines. Here are some key metrics to consider:

• Inference Time: Measure the time taken to process input and produce output.
• Memory Usage: Assess the amount of memory consumed during inference.
• Accuracy: Validate that the quantized model maintains acceptable levels of accuracy.

Supported Models

Gemma 2-2b-it is compatible with various LLMs that can be quantized to int8. This includes popular models like:

• BERT
• GPT-2
• DistilBERT

Limitations

While Gemma 2-2b-it is powerful, it has some limitations:

• Model Compatibility: Only models that support int8 quantization can be used.
• CPU-Only: This implementation does not support GPU acceleration.

Contributing

We welcome contributions to enhance Gemma 2-2b-it. If you would like to contribute, please follow these guidelines:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them with clear messages.
4. Push your branch and create a pull request.

Please ensure that your code adheres to the existing style and includes relevant tests.

License

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

Links

For further information and updates, please visit the Releases section.

Explore the potential of efficient CPU inference with Gemma 2-2b-it. Happy coding!