forFrank/aptos_develop

Aptos-Move-QA-5K: A High-Quality Instruction Dataset for Move on Aptos

Welcome to the Aptos-Move-QA-5K dataset, a specialized collection of over 5,000 high-quality question-and-answer pairs designed to fine-tune Large Language Models (LLMs) into expert assistants for the Move programming language on the Aptos blockchain.

This dataset was curated by Cotrain.AI as a foundational step in our mission to build open, specialized, and community-driven artificial intelligence.

📜 Dataset Overview

The Aptos-Move-QA-5K dataset is a meticulously collected set of instructions and responses focused exclusively on the Move language and its ecosystem within Aptos. It covers a wide range of topics, from basic syntax and concepts to complex subjects like smart contract architecture, security best practices, gas optimization, and interaction with the Aptos framework.

• —Total Records: 5,000+
• —Language: English
• —Domain: Move Programming Language on Aptos
• —Format: Question/Answer Pairs

🎯 Why This Dataset?

While general-purpose LLMs have broad knowledge, they often lack the depth and nuance required for specialized domains like smart contract development. Developers working with Move on Aptos need an AI assistant that provides accurate, safe, and contextually-aware code and explanations.

This dataset solves this problem by providing the high-quality, targeted data necessary to fine-tune models that can:

• —Reduce Errors: Provide correct code snippets and identify common pitfalls.
• —Accelerate Development: Quickly answer complex questions about the Aptos framework and Move standard library.
• —Improve Security: Offer guidance based on established security patterns.
• —Lower Barrier to Entry: Help new developers learn the Move language faster.

格式 (Data Format)

The dataset is provided in a simple and accessible JSON format. Each entry consists of a unique instruction (the question) and the desired output (the answer).

Example Entry:

[
  {
    "instruction": "How do I define a new struct in Move for the Aptos blockchain and make it a storable asset in a user's account?",
    "output": "To define a struct as a storable asset in a user's account on Aptos, you need to give it the `key` ability. This ability signifies that the struct can be stored directly under an account's top-level storage. Here is a basic example:\n\n```move\naddress 0x42;\n\nmodule Example {\n    struct MyAsset has key {\n        id: u64,\n        value: u128\n    }\n\n    public fun create_asset(account: &signer, id: u64, value: u128) {\n        move_to(account, MyAsset { id, value });\n    }\n}\n```\n\nIn this code, the `has key` declaration is crucial. The `move_to` function is then used to place an instance of `MyAsset` directly into the signer's account storage, making it a globally accessible resource for that account."
  }
]

🚀 Potential Use Cases

This dataset is ideal for a variety of powerful applications:

• —Fine-tuning Open-Source LLMs: Fine-tune models like Llama 3, Mistral, or Gemma to create an "Aptos Move expert" model.
• —Developer Chatbots: Build a highly accurate chatbot for Discord, Slack, or documentation websites to answer developer questions in real-time.
• —Advanced Code Completion: Create sophisticated code completion tools for IDEs like VS Code that understand the semantics of Move.
• —Automated Documentation: Power tools that can automatically generate explanations for Move code snippets.

🤝 Powered by Cotrain.AI

The collection and curation of this dataset are part of the Cotrain.AI initiative. We believe that the future of AI is specialized, decentralized, and open. By providing foundational datasets like this, we aim to empower the entire community to build and train powerful AI models collaboratively.

This dataset will be one of the first used within the Cotrain.AI decentralized training network, demonstrating our commitment to improving the tools and intelligence available to the Aptos developer ecosystem.

💡 How to Use

You can easily load this dataset using popular Python libraries like datasets from Hugging Face or pandas.

# Using the Hugging Face datasets library
from datasets import load_dataset

dataset = load_dataset("forFrank/aptos_develop")

🤝 Contributions

While this initial version was curated by Cotrain.AI, we believe in the power of the community. We welcome contributions, corrections, and additions to this dataset. Please feel free to open a pull request or submit an issue on our GitHub repository.

📄 License

This dataset is released under the MIT License. You are free to use, modify, and distribute it for both commercial and non-commercial purposes.