README.md

---
license: apache-2.0
datasets:
- AIAT/Kiddee-data1234
language:
- th
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: table-question-answering
tags:
- code
---
--- 
## license: apache-2.0

![KIDDEE](https://media.discordapp.net/attachments/1226897965927497818/1235837202945151016/KIDDEE-Logoo.png?ex=6635d295&is=66348115&hm=8ea3f9706dcdc7b459919d03d5bdb59c06912425efcff8f3979efa93c9e7549e&=&format=webp&quality=lossless&width=437&height=437)

## Datasets:
  - AIAT/Kiddee-data1234
  - (https://huggingface.co/datasets/AIAT/Kiddee-data1234)
    
## language:
  - th
  - en
## metrics:
  - accuracy 0.53 
  - response time 2.440
    
## pipeline_tag: 
  - table-question-answering

## tags:
- OpenthaiGPT-13b
- LLMModel

# KIDDEE STRONG MUSCLE LLM

This repository contains code and resources for building a Question Answering (QA) system using the Retrieval-Augmented Generation (RAG) approach with the Language Learning Model (LLM).

## Introduction

RAG-QA combines the power of retrieval-based models with generative models to provide accurate and diverse answers to a given question. LLM, a state-of-the-art language model, is used for generation within the RAG framework.

## Features

- **RAG architecture**: Integration of retrieval and generation models.
- **LLM**: Powerful language generation capabilities.
- **Question Answering**: Ability to answer questions based on given contexts.
- **Scalable**: Easily scalable for large datasets and complex questions.
- **Diverse Responses**: Provides diverse responses for a given question through generation.

## Setup

1. Clone this repository:

# I'm not going to tell you


# sponser
![image/png](https://cdn-uploads.huggingface.co/production/uploads/652f721d7a8c08f81e6edfa3/hV0cTKic_YsySNYVrj9b0.png)
  
library_name: adapter-transformers
---