Awesome-Ml-Model-Compression

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awesome-ml-model-compression is an Open Source & Models tool that curates resources for machine learning model compression and acceleration. It includes research papers, articles, tutorials, libraries, and tools.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is awesome-ml-model-compression?

awesome-ml-model-compression is a comprehensive, open-source curated list of resources dedicated to machine learning model compression and acceleration. This GitHub repository compiles research papers, articles, tutorials, libraries, and tools covering various techniques such as quantization, pruning, distillation, and low-rank approximation. It serves as an invaluable reference for researchers, developers, and students looking to optimize deep neural networks for efficiency, speed, and reduced memory footprint. The repository is actively maintained and welcomes contributions, making it a collaborative effort to advance the field of efficient AI model deployment.

Best used for

Ideal for researchers and developers who need to explore state-of-the-art model compression techniques, find relevant academic papers, and discover tools for optimizing deep learning models. Especially valuable for those working on deploying AI models to resource-constrained environments or seeking to improve inference efficiency.

Common actions

compress ML models

accelerate deep learning

optimize neural networks

learn model compression

face swappinggithub copilot"AI Agents"open-sourcedeepfakecollaborationworkflowsautomated workflowlow-code/no-code

Capabilities

Key features

Curated research papers
Quantization techniques
Pruning methods
Distillation strategies
Low-rank approximation

Target Audience

professorresearcherdeveloper

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of model compression techniques are covered in this repository?

The repository covers a wide range of model compression techniques, including quantization (binarization, trained ternary quantization), pruning (filter pruning, dynamic network surgery), distillation (knowledge distillation, sequence-level knowledge distillation), and low-rank approximation (LoRA, QLoRA).

Is this repository actively maintained and open for contributions?

Yes, the awesome-ml-model-compression repository is actively maintained and welcomes contributions. Users are encouraged to submit pull requests to add new research papers, articles, tools, or learning materials to keep the list up-to-date and comprehensive.

Does the list include resources for specific hardware or deployment scenarios?

While not exclusively focused on hardware, the repository includes papers and tools relevant to efficient deployment on mobile devices and other resource-constrained environments. Topics like MobileNets, efficient CNN architectures, and FP8 formats are covered, addressing practical deployment challenges.

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Code Assistants DevOps & Infrastructure No-Code / Low-Code Testing & QA Backend & APIs Prompt Engineering

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Also listed in

This tool also appears in

Research & Education › Academic Research AI Agents & Automation › AI Frameworks & Infra Research & Education › Knowledge Management

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