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Pruna
Overview :
Pruna is a model optimization framework designed for developers. Through a series of compression algorithms, such as quantization, pruning, and compilation, it makes machine learning models faster, smaller, and less computationally expensive during inference. The product is suitable for various model types, including LLMs and vision transformers, and supports multiple platforms such as Linux, MacOS, and Windows. Pruna also offers an enterprise version, Pruna Pro, which unlocks more advanced optimization features and priority support, helping users improve efficiency in practical applications.
Target Users :
Pruna is suitable for machine learning developers, data scientists, and AI researchers who need to quickly optimize and deliver efficient models. With Pruna, users can easily improve model inference speed and reduce resource consumption, which is especially important when handling large-scale data or deploying in resource-constrained environments.
Use Cases
Use Pruna to optimize the model to reduce inference time when generating images using Stable Diffusion.
Quantize and prune large-scale speech recognition models to reduce memory usage.
Utilize the advanced features of Pruna Pro for custom model optimization in real-world applications.
Features
Supports optimization of various model types, including LLMs and vision transformers.
Integrates multiple compression algorithms, such as quantization, pruning, and caching, to improve model performance.
Provides a simple and easy-to-use API; users can optimize models with just a few lines of code.
Access more advanced features and technical support through Pruna Pro.
Allows the collection of non-personal telemetry data to improve product performance.
Includes a built-in evaluation interface to help users test the performance of optimized models.
Supports GPU acceleration to improve computational efficiency.
Provides detailed documentation and community support to facilitate user onboarding and problem-solving.
How to Use
Ensure Python 3.9 or higher is installed.
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Choose to install Pruna using pip, or clone and install from source code.
Load a pre-trained model, such as Stable Diffusion.
Use Pruna's smash function to optimize the model and configure optimization parameters.
Use the evaluation interface to test the performance of the optimized model.
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