einstein-ait-prod

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

einstein-ait-prod

(★ 12)

Supercharge Bittensor Ecosystem with Advanced Mathematical and Logical AI

einstein-ait-prod 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# 🧠 Einstein - AIT 🤖

## Introduction

### Description
Einstein-AIT is a decentralized network utilizing the Bittensor protocol for advanced AI/ML model development focused on mathematics, computational thinking, and data analysis. This document guides validators and miners on setup, operation, and contribution.

- 📚 [Albert Einstein Frontend](https://albert.aitprotocol.ai/)

### Subnet Purpose

Einstein-AIT subnet aims to:

- Host a fine-tuning competition for AI/ML models specialized in mathematics and computational reasoning.
- Produce a revenue-generating subscription business through a front-end Math and Data analytics LLM application, leveraging RLFH via real user feedback and response rating.
- Open-source and fine-tune a custom AI/ML model trained on a robust dataset, including a cleaned MIT-IDSS mathematics dataset.

### Subnet Objective

To provide real-world users with an ever-improving AI/ML model capable of computing complex mathematics and data analytics, adapting to the user's education level, and providing appropriate justifications for its results.

### Subnet Product Breakdown

- **Open Source**: Specialized mathematics ML model trained on various datasets, including proprietary data from MIT Institute for Data Systems and Society (IDSS).
- **Hugging Face Leaderboard**: For fine-tuning the model and allowing miners to compete with custom models.
- **Front-End Application**: Revenue-generating business model.

### Key Features
- 🚀 **Advanced Computational Network**: Incentivizing miners to enhance computational resources for complex AI/ML tasks.
- 📈 **Performance Commitment**: Miners commit to a model type and task volume.
- 💰 **Incentive Mechanism**: 
  - `reward = (0.6 * accuracy_score) + (0.3 * reasoning_score) + (0.1 * time_score)`
  - `accuracy_score` is based on the correctness of the result.
  - `reasoning_score` measures the similarity between the generated text and reference data.
  - `time_score` is derived from the time taken to generate the result.
- 🌟 **Continuous Improvement**: Introducing new models and features based on demand.
- 💵 **Earn as a Validator**: Validators earn by sharing computational resources with miners.

## Setup and Participation

### For Validators
1. 🛠️ Install dependencies and set up the Einstein-AIT validator node.
2. ⚙️ Configure validator settings, including TAO staking.
3. 🚀 Start the validator node and process AI/ML tasks from miners.

Detailed instructions for setting up the validator node can be found [here](docs/validator.md).

### For Miners
1. 🛠️ Install dependencies and set up the Einstein-AIT miner node.
2. 🗂️ Choose a model type and specify task volume.
3. 🚀 Start the miner node and contribute computational resources.

Detailed instructions for setting up the miner node can be found in the miners documentations:
- [OpenAI Miner](neurons/miners/openai/README.md)
- [HuggingFace Miner](neurons/miners/huggingface/README.md)

## Contribution
We welcome contributions to the Einstein-AIT project! If you have ideas, bug reports, or feature requests, please open an issue on our GitHub repository. To contribute code, fork the repository and submit a pull request.

## License
This repository is licensed under the MIT License. See [LICENSE](./LICENSE) for details.

einstein-ait-prod

einstein-ait-prod 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

einstein-ait-prod

einstein-ait-prod 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README