README.md

metadata

title: Algorithm Finder
emoji: 🔍
colorFrom: blue
colorTo: blue
sdk: gradio
sdk_version: 6.19.0
python_version: '3.10'
app_file: app.py
pinned: false
hardware: zero-gpu

Algorithm Finder

Find the best algorithm for any code/math problem using LLM-powered C++ agents, compiled and benchmarked locally (Zero GPU).

How It Works

1. Initial Coder: Generates a compilable C++ solution for your problem.
2. Researcher: Generates N novel alternative C++ algorithms.
3. Validator: Evaluates all candidates against performance metrics and selects a winner.
   • If no winner passes, it regenerates N new algorithms (up to max_validation_rounds).
4. Real-World Tester: Tests the winner against the baseline in N realistic scenarios.
5. Summary: Displays a Markdown summary of the winning algorithm and results.

Setup

Local

pip install -r requirements.txt
python app.py

Hugging Face Spaces (Zero GPU)

1. Create a new Space with the Gradio SDK.
2. Set app.py as the entrypoint.
3. Add requirements.txt.
4. (Optional) Add your HF_TOKEN as a Space secret.
5. Deploy.

Usage

• Problem Description: Describe the problem you want to optimize.
• Objective: What you are trying to optimize (speed, memory, accuracy, etc.).
• User-defined Metric: A custom metric the validator should prioritize.
• Model ID: Any Hugging Face Instruct model (e.g., meta-llama/Meta-Llama-3-8B-Instruct).
• Number of candidate algorithms: How many new algorithms the researcher generates per round.
• Number of real-world scenarios: How many scenarios the winner is tested in.
• Max validation rounds: How many times to regenerate if no winner is found.

Notes

• This app uses the Hugging Face Inference API (free tier or Pro) to run LLMs.
• C++ compilation happens locally using g++.
• Execution time and memory usage are measured for each algorithm.
• All code is generated and executed within the app sandbox.