What is OptiML?
OptiML is an Apache 2.0 licensed open-source project designed to seamlessly integrate powerful compression techniques into fine-tuning workflows. It empowers AI developers to optimize models during fine-tuning, making them smaller, faster, and more efficient—without sacrificing accuracy. OptiML also provides a flexible platform for researchers to experiment with cutting-edge compression methods on any model.
Why use OptiML?
Improve Performance and Reduce Costs
- Desired Accuracy with Lower Costs: Produce models that achieve the desired accuracy while significantly reducing inferencing costs.
- Sustainable AI: Reduce energy consumption, supporting sustainable AI practices.
Easily Integrate in MLOps Pipelines
- Rapid Iteration: Quickly test and iterate on different compression strategies.
- Optimized Fine-Tuning: Enhance fine-tuning by ensuring optimal performance with lower complexity.
Support any Hardware
- Versatile Compatibility: Leverage compression gains on any hardware platform, from edge devices to cloud infrastructure.
- Consistent Deployment: Deploy compressed models across various environments with consistent performance.
Research New Compression
- Experiment with new approaches and easily evaluate on any model and benchmark against state-of-the-art.
Why use OptiML?
Cut Costs, Boost Performance
Combine fine-tuning with state-of-the-art compression to optimize any model. Reduce complexity, lower inferencing costs, and enhance performance—all without compromising accuracy.
Sustainable AI: Reduce energy consumption, supporting sustainable AI practices.
Easily Integrate in AI Pipelines
Incorporate compression directly into fine-tuning workflows without disrupting your pipeline. Seamlessly test, iterate, and optimize models for efficiency and performance.
Optimized Fine-Tuning: Produce smaller fine-tuned models without sacrificing accuracy.
Support any Hardware
Optimize models with compression techniques that work across all platforms, from edge devices to cloud infrastructure, ensuring compatibility and performance gains everywhere.
Consistent Deployment: Deploy compressed models across various environments with consistent performance.
Research New Compression
Explore and test innovative compression techniques on any model, with tools to benchmark against state-of-the-art methods effortlessly.
Why use OptiML?
Improve Performance and Reduce Costs
Desired Accuracy with Lower Costs: Produce models that achieve the desired accuracy while significantly reducing inferencing costs.Sustainable AI: Reduce energy consumption, supporting sustainable AI practices.
Easily Integrate in MLOps Pipelines
Rapid Iteration: Quickly test and iterate on different compression strategies.Optimized Fine-Tuning: Enhance fine-tuning by ensuring optimal performance with lower complexity.
Support any Hardware
Versatile Compatibility: Leverage compression gains on any hardware platform, from edge devices to cloud infrastructure.Versatile Compatibility: Leverage compression gains on any hardware platform, from edge devices to cloud infrastructure.
Research New Compression
Experiment with new approaches and easily evaluate on any model and benchmark against state-of-the-art.
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OptiML
OptiML
