OptiML

The Open-Source Library for Fine-Tuning with Compression

What is OptiML?

OptiML is an Apache 2.0 licensed open-source platform that curates and integrates state-of-the-art model compression techniques directly into the fine-tuning pipeline. By combining advanced compression methods with task-specific optimization, OptiML enables the efficient deployment of large language models without compromising accuracy. In an era where model sizes and deployment costs are escalating rapidly, OptiML addresses the growing demand for practical and accessible AI compression solutions.

OptiML unifies diverse quantization and pruning techniques into a single, coherent framework. From static optimizations for immediate deployment to dynamic compression during fine-tuning, OptiML provides flexible and adaptive solutions to meet a wide range of deployment requirements. The platform is designed to significantly reduce computational and memory overhead while maintaining or enhancing model performance.

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

Task-Aware Analysis

Monitors gradient flows and parameter updates during fine-tuning to assess component importance.
Builds comprehensive layer statistics and attention pattern profiles to understand model behavior.
Generates parameter sensitivity maps to identify critical components.
Tracks temporal patterns in weight updates and activation distributions for deeper insights.

Importance Profiling

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 Orchestration

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.

Combines multiple importance signals, including:
First and second-order optimization statistics.
Dynamic range analysis for effective quantization.
Propagation of compression effects across the network.
Adapts importance scores based on task-specific metrics, ensuring robust and context-aware profiling.

Implements targeted compression guided by importance profiling:
Adjusts compression ratios to component sensitivity.
Preserves critical pathways while aggressively compressing redundant components.
Leverages cutting-edge techniques:
AWQ, GPTQ, SparseGPT, Wanda for proven compression methods.
Structured and unstructured pruning across multiple granularities.
Mixed-precision quantization with importance-aware bit allocation.
Continuously monitors task performance, dynamically adjusting compression strategies to maintain accuracy.

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