Here is an article about Ethereum bridge data in the chain with LLM models:
Data toe with the chain with LLM models: token information recovery in solidity
As a developer who builds a Token, he is constantly looking for tours to improve Torry’s experience and simplify the functionality of his apclation. In an innovative approach, it improves the efficiency of its program to integrate large language models (LLM) as GPT in Dateval in Quinatina. In this article, we will explore how to close the data in the chain with LLM models in solidity.
What are the LLM?
Large language models, Souch as models based on Google transformers, have revolutionized the acquisition of natural language and is not shown in Showius in Varioous. They consist of a Massataaset of texts, it is the learning to process and generate and I like answers. These models have numerous advantages over traditional information recovery methods:
- Speed : LLMS channels large amounts of data in a fraction of time that are traditional methods.
- ACCury : Because of leverage of large data, the LLM can provide precise answers to complex consultations.
- Escaliability
: With a set of mass training data, the LLMs can make volumes of rare.
Challenges and limitations
While the LLMs are excellent for data recovery of the chain solution, there are several challenges and limitations to consult:
- Data requirements : Construction and the main data data for LLMS have been very expensive.
- Data quality : Ensure the precision and relevance of the responses generated require high quality.
– Token ic.
Put data on the chain with LLM * models
Teddress Tees, we will focus on Creaty a bridge between the data in the chain and the LLM models. This world is permitted to users to consult the information of Token’s information that the application uses Tanguage natural language.
Here is an example of the house in the implementation of this in solidity:
`SOLIDITY
Pragma solidity ^0.8.0;
Tokeninfo of counterattack
Mapping (address => uint256) public balances;
Function of balance (user of address) internal view returns (uint256) {
Return balances [user];
}
Betweentokenmined Function (Uint256 _MINDTOKEN) Internal view returns (bool) {
// This is a position marker for character
// You can implement this logic according to your token requirements
Bool aquedrado = true;
return coined;
}
}
SOLIDITY
Pragma solidity ^0.8.0;
Contract bridge {
Eddress Tokenaddress public; // Theddress of the Token you want to consult
Stroct Tokendata {
Balance Uint256;
Uint256 MINTEDTOKEN;
}
Function of balance (user of address) internal view returns (uint256) {
Return bridges [tokenaddress] [usor] .balance;
}
Betweentokenmined Function (Uint256 _MINDTOKEN) Internal view returns (bool) {
Return bridges [tokenaddress] [_ mintoken] .mined;
}
}
BridgeManager hired {
Bridge [] Public Bedias; // Store the bridge mapping
Builder () {
bridges.push (bridge (0x ...)); // Reland with Token's address
}
Balance function (address user, uint256 _token) internal view returns (Uint256) {
Tokendata data = bridges [user] [_ token];
return data.
}
FUNCTION HEETTOKENMEDED (UINT256 _TOKEN, UINT256 _MINDTOKEN) INTERNAL RETURNS (BOOL) {
Tokendata data = bridges [_token] [_ mintoDtoken];
return data.
}
}
`
In this example, we have created a ‘bridge’ contract, which acts as a bridge between the data of the chain and the LLM models.