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Are you fascinated by the capabilities of Hugging Face models but unsure how to run them locally?  Look no further!  Here, we will explore the simplest and most effective way to get Hugging Face models up and running on your local machine using Ollama . For a complete walkthrough check out my latest video on "How to Run Hugging Face Models Locally Using Ollama".  This video covers everything from installation to running an example, ensuring you have all the information you need to get started: Happy coding!

In the rapidly evolving field of artificial intelligence, generating accurate and contextually relevant responses is crucial. Ollama , a lightweight and extensible framework, combined with the powerful Llama 3.2 model, provides a robust solution for generating AI model responses in JSON format. This article explores how to leverage these tools to create efficient and effective AI responses. In case, if you are interested in knowing every single bit then here is my video recording: Setting Up Ollama and Llama 3.2 Before diving into the specifics of generating responses, it's essential to set up Ollama and Llama 3.2 on your local machine. Ollama offers a straightforward installation process, and you can download the necessary models from the Ollama library.  Import required packages In order to get started with code, first we need to import the required packages: from ollama import chat from pydantic import BaseModel Generating Responses in JSON Format JSON format is a structure...

In the age of cutting-edge technology, the ability to clone your voice is no longer a futuristic dream. With advancements in Text-to-Speech (TTS) technology, you can create a digital replica of your voice using open-source tools like SWivid's F5-TTS. Whether you're a tech enthusiast, a content creator, or someone interested in preserving their voice, this guide will walk you through the process step-by-step. If you're interested in watching, then here is the recording: What is SWivid's F5-TTS? SWivid's F5-TTS is an open-source Text-to-Speech system that uses deep learning algorithms to synthesize speech. It leverages a powerful neural network to create highly realistic and natural-sounding voices.  The best part?  It’s accessible to anyone with a bit of tech know-how and a willingness to experiment. Why Clone Your Voice? Cloning your voice can have numerous applications: Accessibility: Create personalized voice assistants. Content Creation: Enhance your videos, podc...

In this article we will see how we can run our agents locally which means we will be using OpenAI Swarm framework but still we will not be paying anything to OpenAI as we will not be utilizing OpenAI's API key. Using OpenAI's Swarm but not using OpenAI's key, got confused?  Well, we will be achieving this using Ollama :) Now, before we proceed, if you have not watched my earlier video on what is OpenAI-Swarm and how to get started with it, I would recommend you check this one here:  Here is link of the GitHub repository containing Swarm's source code and implementation details. What Are We Trying To Do? We will create our own agent utilizing  OpenAI-Swarm framework using Ollama and the open-source model Llama3.2:1b . This agent will run locally on our machine without the need to any API key from OpenAI . Setting Up The Things Install Swarm We need to install Swarm from GitHub as it is still in experimental stage and that can be done by running below command: pip...

In the ever-evolving landscape of data management, the ability to generate SQL queries without a predefined schema can significantly enhance flexibility and efficiency. Leveraging the power of GPT-3.5 Turbo , we can achieve this by fine-tuning the model to understand and generate schema-free SQL queries.  Why Schema-Free SQL Queries? Traditional SQL queries rely on a predefined schema, which can be limiting in case of huge number of tables or columns. Here are the few benefits of using schema-free approach: Efficiency: By not requiring schema definitions in every prompt, you save on prompt size and reduce the complexity of your queries, making the process faster and more efficient. User-Friendly: It simplifies the querying process for users who may not be familiar with the database schema, enabling them to retrieve data using natural language descriptions. Adaptability: GPT models can adapt to various database structures and types, making them versatile tools for querying differen...

In today’s data-driven era, where we are dealing with massive data pertaining to different data sources having different data formats, it creates a very big challenge, especially in terms of efficient search and information retrieval. Dive into this deep ocean and locating the correct data is not at all an easy task with AI. In this article, I’ll show you how you can integrate Azure SQL Database, FAISS , and advanced models from Hugging Face to enhance your search capabilities. Let’s take a quick look at each of these pillars. Understanding Terminology Azure SQL Database Azure SQL Database i s a cloud-based service that provides a totally managed database system. The high availability, scalability, and security of this particular service type make it the best option for handling large amounts of data. FAISS FAISS is short for Facebook AI Similarity Search. It is a library written by Facebook AI Research that enables efficient similarity search and clustering of dense vectors. It suppor...