oripress/AlgoTune
AlgoTune is a NeurIPS 2025 benchmark made up of 154 math, physics, and computer science problems. The goal is write code that solves each problem, and is faster than existing implementations.
AlgoTune helps you benchmark how well large language models can optimize code for common math, physics, and computer science functions. You provide existing code and select an AI model; AlgoTune then outputs new code versions and detailed speed-up reports. This is for researchers and engineers who want to assess or improve the performance optimization capabilities of AI models.
Use this if you need to systematically evaluate how effectively large language models can generate faster, equivalent code for numerical problems.
Not ideal if you are looking for a tool to automatically fix bugs in your existing code or to generate code from natural language prompts without a focus on performance optimization.
Stars
95
Forks
13
Language
Python
License
MIT
Category
Last pushed
Mar 12, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/oripress/AlgoTune"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related models
xjywhu/Awesome-Multimodal-LLM-for-Code
Multimodal Large Language Models for Code Generation under Multimodal Scenarios
jie-jw-wu/human-eval-comm
HumanEvalComm: Evaluating Communication Skill of Code LLM and LLM Agent
juyongjiang/CodeUp
CodeUp: A Multilingual Code Generation Llama-X Model with Parameter-Efficient Instruction-Tuning
JHansiduYapa/Fine-Tuning-a-Small-Language-Model-for-Cypher-Query-Generation
This project fine-tunes Unsloth's Gemma-3 4B IT (4-bit) model to translate natural language into...
Gen-Verse/ReasonFlux
[NeurIPS 2025 Spotlight] LLM post-training suite — featuring ReasonFlux, ReasonFlux-PRM, and...