Data and code for FreshLLMs (https://arxiv.org/abs/2310.03214)
-
Updated
Nov 25, 2024 - Jupyter Notebook
Data and code for FreshLLMs (https://arxiv.org/abs/2310.03214)
pyWhat LLM version | Answer "What is it?" on the command line with the power of large language models
The course provides guidance on best practices for prompting and building applications with the powerful open commercial license models of Llama 2.
Leveraged the power of Google Cloud's Vertex AI platform to develop advanced Large Language Models (LLMs). Utilizing the Python API provided by Google Cloud, this endeavor represents a significant stride in the realm of natural language processing and LLMs.
Dynamic Few-Shot Prompting is a Python package that dynamically selects N samples that are contextually close to the user's task or query from a knowledge base (similar to RAG) to include in the prompt.
Dynamic Few-Shot Prompting for Customer Support AI Agents A practical implementation of dynamic few-shot prompting using LangChain and HuggingFace models. This repository provides an optimized approach to improving AI agent performance for customer support tasks by selecting relevant examples based on user queries, thus enhancing response accuracy
This repository contains results from my MSc. thesis on "Test Case Generation from User Stories using Generative AI Techniques with LLM Models." Each folder includes generated test cases in PDF, detailed metrics scores of data in Excel sheets, and visual graphs, offering a comprehensive view of the experiments in images folder and their outcomes.
This repository contains Jupyter notebooks demonstrating various generation tasks with Large Language Models (LLMs). It provides examples for summarization, text generation, few-shot learning, and translation, utilizing different LLM APIs to showcase the capabilities of multiple providers.
This is GenAI based ShopAssist Application which is to recommend laptops to the user absed upon their filtered out requirements
Python Project Sample for Demonstration
Unlocking the Power of Generative AI: In-Context Learning, Instruction Fine-Tuning and Reinforcement Learning Fine-Tuning.
The study explores zero-shot and few-shot prompting strategies using Meta's quantized LLaMA 3.1 70B model to perform Named Entity Recognition (NER) on Nepali text.
Explanation of Programming Errors using Open-source LLMs
Add a description, image, and links to the few-shot-prompting topic page so that developers can more easily learn about it.
To associate your repository with the few-shot-prompting topic, visit your repo's landing page and select "manage topics."