OptiML

The Open-Source Library for Fine-Tuning with Compression

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.

Pioneering AI Innovation with the Support of Leading Teams Worldwide.

How OptiML Works

During fine-tuning, the network adapts its weights and activations to the specific downstream task.

OptiML monitors these changes to identify which parts of the network are actively contributing to task performance.

By analyzing patterns like weight updates, gradients, or activations, OptiML pinpoints the parts of the network — such as specific layers, neurons, or parameters — that are most relevant.
It identifies redundant or less impactful components that can be optimized without harming performance.

While identifying the "important" parts of the network, OptiML applies compression techniques such as:

Pruning: Removing unimportant weights or neurons.
Quantization: Reducing numerical precision to save memory and computation.

This iterative process results in a leaner, faster, and more efficient model tailored specifically for the downstream task.

How OptiML Works

Fine-Tuning Awareness

During fine-tuning, the network adapts its weights and activations to the specific downstream task.
OptiML monitors these changes to identify which parts of the network are actively contributing to task performance.

Importance Determination

End-to-end deployment optimized for real-time, scalable production across cloud and edge environments.
Models are fully optimized for seamless production, reducing latency and maximizing throughput.

Compression Integration

Attention. By integrating techniques like mixed precision, 2:4 sparsity, efficient key-value caching, dynamic batching, and kernel fusion, we maximize performance while maintaining accuracy, ensuring models are production-ready with minimal latency.

By analyzing patterns like weight updates, gradients, or activations, OptiML pinpoints the parts of the network — such as specific layers, neurons, or parameters — that are most relevant.
It identifies redundant or less impactful components that can be optimized without harming performance.

While identifying the "important" parts of the network, OptiML applies compression techniques such as:
Pruning: Removing unimportant weights or neurons.
Quantization: Reducing numerical precision to save memory and computation.
This iterative process results in a leaner, faster, and more efficient model tailored specifically for the downstream task.

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