Data and code for FreshLLMs (https://arxiv.org/abs/2310.03214)
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Updated
Nov 4, 2024 - Jupyter Notebook
Data and code for FreshLLMs (https://arxiv.org/abs/2310.03214)
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.
Unlocking the Power of Generative AI: In-Context Learning, Instruction Fine-Tuning and Reinforcement Learning Fine-Tuning.
Explanation of Programming Errors using Open-source LLMs
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 is GenAI based ShopAssist Application which is to recommend laptops to the user absed upon their filtered out requirements
Python Project Sample for Demonstration
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.
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.
pyWhat LLM version | Answer "What is it?" on the command line with the power of large language models
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