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This is a project done for an assessment. I found it to be interesting and decided to share this. The idea is to create a scraper to scrap the Wikipedia page and generate question and answers
Performing Prompt engineering on a dialogue summarization task using Flan-T5 and the dialogsum dataset. Exploring how different prompts affect the output of the model, and compare zero-shot and few-shot inferences.
Fine Tuning pegasus and flan-t5 pre-trained language model on dialogsum datasets for conversation summarization to to optimize context window in RAG-LLMs
Performing the task of dialogue summarisation using Generative AI, whilst comparing the effects of zero shot, one shot and few shot prompt engineering. These steps are used to enhance the completion of Large Language Models (LLMs))
The LLM-based medical chatbot, powered by the Llama-2-7b-chat-hf model from Meta and implemented within the Langchain framework, offers personalized healthcare support.
Discussed about 4 use-cases or case studies. Discussed about the approaches and significance of these use-cases as these are different from others. There are several approaches available which can be done using LLM but here the approaches and it's significance could bring insightful approaches towards it's execution.
This repository was commited under the action of executing important tasks on which modern Generative AI concepts are laid on. In particular, we focussed on three coding actions of Large Language Models. Extra and necessary details are given in the README.md file.