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  ![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)
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  ## Tag
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- - OpenThaiGPT13b
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  ## Datasets:
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  - (https://huggingface.co/datasets/AIAT/Kiddee-data1234)
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  - OpenthaiGPT-13b
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  - LLMModel
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- # KIDDEE STRONG MUSCLE LLM
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  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).
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  ## Introduction
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  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.
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- ## Features
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-
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- - **RAG architecture**: Integration of retrieval and generation models.
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- - **LLM**: Powerful language generation capabilities.
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- - **Question Answering**: Ability to answer questions based on given contexts.
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- - **Scalable**: Easily scalable for large datasets and complex questions.
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- - **Diverse Responses**: Provides diverse responses for a given question through generation.
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- ## Setup
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- 1. Clone this repository:
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- # I'm not going to tell you
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-
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  # sponser
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  ![image/png](https://media.discordapp.net/attachments/1226897965927497818/1235842881520930857/image.png?ex=6635d7df&is=6634865f&hm=be4eb57b51de9f52f0817a88fdd2461b5312d0a013bd022630b2a8dde717976f&=&format=webp&quality=lossless&width=687&height=402)
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  ![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)
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  ## Tag
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+ - openthaigpt/openthaigpt-1.0.0-13b-chat
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  ## Datasets:
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  - (https://huggingface.co/datasets/AIAT/Kiddee-data1234)
 
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  - OpenthaiGPT-13b
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  - LLMModel
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  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).
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  ## Introduction
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  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.
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  # sponser
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  ![image/png](https://media.discordapp.net/attachments/1226897965927497818/1235842881520930857/image.png?ex=6635d7df&is=6634865f&hm=be4eb57b51de9f52f0817a88fdd2461b5312d0a013bd022630b2a8dde717976f&=&format=webp&quality=lossless&width=687&height=402)
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