LLM4AD

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LLM4AD is an open-source platform leveraging Large Language Models for automatic algorithm design. It provides unified interfaces and evaluation acceleration for various optimization and machine learning tasks.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is LLM4AD?

LLM4AD is an open-source, Python-based platform designed for Automatic Algorithm Design (AD) using Large Language Models (LLMs). It offers a comprehensive framework for researchers and developers to test, build, and deploy LLM4AD applications. The platform includes unified interfaces for methods, tasks, and LLMs, alongside features like evaluation acceleration, secure evaluation with process protection, and robust logging capabilities (local, Wandb, Tensorboard). While originally developed for optimization tasks, LLM4AD is versatile enough for applications in machine learning, science discovery, game theory, and engineering design. It supports various LLM interfaces, including remote APIs and local HuggingFace deployments, and provides a GUI for enhanced usability.

Best used for

Ideal for developers and data scientists who need to automate the design of algorithms for optimization, machine learning, or scientific discovery. Especially valuable for researchers looking to leverage LLMs for complex problem-solving and rapid prototyping of new heuristics.

Common actions

design algorithms

automate algorithm creation

evaluate heuristics

integrate large language models

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Capabilities

Key features

Automatic algorithm design
Unified LLM interfaces
Evaluation acceleration
Secure evaluation
Comprehensive logging
GUI for interaction
Resume run functionality

Target Audience

developerdata scientiststudent

Integrations

wandbtensorboard

Pricing & Plans

Open Source

Free

FAQs

What kind of LLMs can I use with LLM4AD?

LLM4AD supports various LLM interfaces, including remote APIs like GPT4o, GPT3.5, Gemini Pro, and Deepseek. It also allows for local HuggingFace LLM deployments such as Llamacode, Llama, and Gemma, providing flexibility for users.

What are the system requirements for installing LLM4AD?

LLM4AD requires Python version 3.9 or higher, but less than 3.13. It is recommended to install and run LLM4AD within a conda environment. Additional packages like gym, numba, tensorboard, and wandb may be needed depending on specific use cases.

Can LLM4AD be used for tasks beyond optimization?

Yes, while originally developed for optimization, LLM4AD's framework is versatile. It can be applied to other areas including machine learning tasks (e.g., adversarial attack strategies), science discovery (e.g., turbulence model design), game theory, and engineering design.

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