MathBlackBox

A deep learning model that explores black-box approaches to solving mathematical problems.

CommonProductProgrammingDeep LearningMathematical Model

MathBlackBox is a deep learning model designed to explore black-box methods for solving mathematical problems. It utilizes VLLM or other OpenAI-compatible approaches, conducts inference through the Huggingface toolkit and OpenAI, supports operation within Slurm environments, and can process various datasets. This project is currently in its early stages and requires thorough testing before deployment in real-world applications.

Create OpenAI-compatible servers in Slurm or non-Slurm environments
Support multiple datasets
selectable via the DATA_DIR_NAME parameter
Run all datasets using run_olympics.py
Support early stopping mechanism with run_with_earlystopping.py
Server and client environment configuration guide
Provide detailed usage instructions and precautions

Target audience: data scientists
machine learning researchers
and deep learning engineers who require a model capable of handling complex mathematical problems while delivering fast and accurate solutions.

Researchers utilizing the MathBlackBox model for experiments solving mathematical problems
Data scientists leveraging the model for large-scale mathematical calculations on Slurm clusters
Machine learning engineers integrating the model into existing mathematical problem-solving systems

1. Ensure VLLM or other OpenAI-compatible methods are installed in your environment.
2. Install the Huggingface toolkit and OpenAI library for inference.
3. Configure the server environment based on whether you are using a Slurm environment.
4. Prepare your dataset and set the DATA_DIR_NAME parameter.
5. Run datasets for model training using run_olympics.py.

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Monthly Visits

494758773

Bounce Rate

37.69%

Page per Visit

5.7

Visit Duration

00:06:29

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