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<pclass="card-text post-summary">Introduction Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances large language models (LLMs) with the ability to access and utilize external knowledge. This guide will walk you through a practical implementation of RAG using Python and various libraries, explaining each component in detail.
1. Introduction Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances large language models (LLMs) with the ability to access and utilize external knowledge. This guide will walk you through a practical implementation of RAG using Python and various libraries, explaining each component in detail.
<description>Introduction Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances large language models (LLMs) with the ability to access and utilize external knowledge. This guide will walk you through a practical implementation of RAG using Python and various libraries, explaining each component in detail.
1. Introduction Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances large language models (LLMs) with the ability to access and utilize external knowledge. This guide will walk you through a practical implementation of RAG using Python and various libraries, explaining each component in detail.
<pclass="card-text post-summary">Introduction Quantum teleportation is a fundamental protocol in quantum information science that enables the transfer of quantum information from one location to another. Despite its name, it doesn’t involve the transportation of matter, but rather the transmission of the quantum state of a particle.
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The Concept In quantum teleportation, we have three main parties:
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Alice: The sender who wants to transmit a quantum state. Bob: The receiver who will receive the quantum state.</p>
<pclass="card-text post-summary">Introduction to Quantum Computing Quantum computing represents a transformative leap in computational technology. Unlike classical computers, which use bits as the smallest unit of data, quantum computers employ quantum bits, or qubits. These qubits take advantage of the principles of quantum mechanics, allowing for exponentially greater processing power in certain types of computations.
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Core Concepts:
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Superposition: Unlike classical bits that can be either 0 or 1, qubits can exist in a state that is a superposition of both.</p>
<pclass="card-text post-summary">Introduction Quantum teleportation is a fundamental protocol in quantum information science that enables the transfer of quantum information from one location to another. Despite its name, it doesn’t involve the transportation of matter, but rather the transmission of the quantum state of a particle.
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The Concept In quantum teleportation, we have three main parties:
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Alice: The sender who wants to transmit a quantum state. Bob: The receiver who will receive the quantum state.</p>
<pclass="card-text post-summary">Introduction to Quantum Computing Quantum computing represents a transformative leap in computational technology. Unlike classical computers, which use bits as the smallest unit of data, quantum computers employ quantum bits, or qubits. These qubits take advantage of the principles of quantum mechanics, allowing for exponentially greater processing power in certain types of computations.
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Core Concepts:
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Superposition: Unlike classical bits that can be either 0 or 1, qubits can exist in a state that is a superposition of both.</p>
<h5class="card-title">Florence-2 - Vision Foundation Model - Examples</h5>
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<pclass="card-text post-summary">Install dependencies Type the following command to install possible needed dependencies (especially if the inference is performed on the CPU)
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%pip install einops flash_attn In Kaggle, transformers and torch are already installed. Otherwise you also need to install them on your local PC.
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Import Libraries from transformers import AutoProcessor, AutoModelForCausalLM from PIL import Image import requests import copy import torch %matplotlib inline Import the model We can choose Florence-2-large or Florence-2-large-ft (fine-tuned).</p>
1. Introduction Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances large language models (LLMs) with the ability to access and utilize external knowledge. This guide will walk you through a practical implementation of RAG using Python and various libraries, explaining each component in detail.
<pclass="card-text post-summary">Introduction Retrieval-Augmented Generation (RAG) is an advanced AI technique that enhances large language models (LLMs) with the ability to access and utilize external knowledge. This guide will walk you through a practical implementation of RAG using Python and various libraries, explaining each component in detail.
<h5class="card-title">Florence-2 - Vision Foundation Model - Examples</h5>
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<pclass="card-text post-summary">Install dependencies Type the following command to install possible needed dependencies (especially if the inference is performed on the CPU)
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%pip install einops flash_attn In Kaggle, transformers and torch are already installed. Otherwise you also need to install them on your local PC.
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Import Libraries from transformers import AutoProcessor, AutoModelForCausalLM from PIL import Image import requests import copy import torch %matplotlib inline Import the model We can choose Florence-2-large or Florence-2-large-ft (fine-tuned).</p>
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