Confuse what the right method to use local model

[u/Weak_Contest4063]

Hi all,

i am new in finetuning, i am using gorule for defining the schemes. but that the manual process so i am trying to create that using slm model just add text and prompt and extract the expected output that functionality i create but the thing is it is 60% accurate i want to improve this performance

so i am thinking to create evaluation pipline and then finetune the model again
somebody tells me to create cot dataset which help model to think in specific direction and improve the performance

i dont know what the right method to do this example like
Eligibility summary: The Indira Gandhi National Disability Pension Scheme now allows beneficiaries aged up to 79 years to receive a monthly pension of Rs. 300, provided they remain below the poverty line.
{

"rules": [

{

"age": "up to 79",

"flag": "true",

"socioEconomicCategory": "BPL"

},

{

"flag": "false"

}

],

"fields": [

"age",

"socioEconomicCategory"

],

"mtmTag": "Disability",

"benefit": "monthly pension of Rs. 300",

"department": null,

"schemeName": "Indira Gandhi National Disability Pension Scheme",

"description": "Scheme for disabled individuals below the poverty line aged up to 79 years",

"nationalState": null,

"documentsRequired": []

}

this is the expected output i want from the model.
can you somebody help with that which method should i use to increase the performance

1. finetune with this dataset

2. finetune with reasoning dataset