New course with Hugging Face: Open Source Models with Hugging Face

DeepLearningAI
4 Mar 202403:06

TLDRThis video introduces an exciting partnership with Hugging Face, highlighting the transformative impact of their open-source tools on AI development. The course teaches best practices for rapidly deploying a variety of pre-trained models to create innovative AI applications. It demonstrates how to combine models for tasks such as assisting visually impaired individuals by describing images, using object detection and text-to-speech models. The course covers searching and selecting from thousands of models on the Hugging Face Hub, interacting with them via the Transformers Library, and wrapping AI applications in user-friendly interfaces for broader accessibility.

Takeaways

  • 🤖 Open-source models integrated with Hugging Face are introduced as a powerful tool for AI Builders.
  • 🚀 The partnership with Hugging Face has made AI applications more accessible through their transformative tools.
  • 📚 The course teaches best practices for quickly deploying a variety of trained open-source models for different AI applications.
  • 🌐 Models can be assembled to create new applications, such as image recognition, language models, and speech recognition.
  • 👁️‍🗨️ An example application is assisting visually impaired individuals by describing images aloud using object detection and text-to-speech models.
  • 💡 All models used in the course are open source, meaning they are freely available for anyone to use and download.
  • 🔍 The course provides guidance on searching for and selecting appropriate models from the Hugging Face Hub.
  • 🛠️ The Hugging Face Transformers library and pipeline objects simplify the process of building AI applications with pre and post-processing.
  • 📱 AI applications can be wrapped in user-friendly interfaces and deployed as APIs for internet accessibility.
  • 🗣️ A voice assistant can be created by combining automatic speech recognition and text-to-speech models.
  • 🌐 The course aims to unlock opportunities for building AI-powered applications by leveraging the power of open-source models.

Q & A

  • What is the main focus of the course introduced in the transcript?

    -The course focuses on teaching best practices for quickly assembling AI applications using a variety of pre-trained, open-source models available through Hugging Face, including handling text, audio, and images.

  • How does the course plan to utilize Hugging Face's tools?

    -The course will utilize Hugging Face's Transformers library and open-source models to demonstrate building AI applications, such as an image recognition tool for the visually impaired, by combining models for processing text, audio, and images.

  • What is an example of an application that can be built during the course?

    -An example application is an image narration assistant for the visually impaired, which uses object detection and text-to-speech models to describe images aloud.

  • Are the models used in the course accessible for everyone?

    -Yes, all the models used in the course are open-source, meaning their models and weights are freely available for anyone to download and use.

  • Who are the instructors of the course?

    -The instructors are Unice Bod, Mark Sun, and Maria Halis, all of whom are affiliated with Hugging Face.

  • What will students learn about interacting with models in the course?

    -Students will learn to interact with models using the 'pipeline' object from the Hugging Face Transformers Library, which simplifies the pre-processing of inputs and post-processing of outputs.

  • How does the course intend to make AI applications user-friendly?

    -The course will teach students to wrap their AI applications in a user-friendly interface using the Gradio library, enhancing accessibility and usability.

  • What is the purpose of deploying an AI-enabled image captioning service as an API?

    -Deploying as an API allows anyone with internet access to make API calls to use the application, thereby broadening the reach and utility of the built AI application.

  • How does the course aim to integrate voice assistant capabilities?

    -By combining automatic speech recognition and text-to-speech models, the course aims to teach students how to build components that can be integrated into a voice assistant.

  • What types of tasks will students learn to perform with open-source models?

    -Students will learn to perform various natural language tasks such as summarizing text, translating languages, and interacting in a chat-like manner, leveraging the capabilities of large language models (LLMs).

Outlines

00:00

🤖 Introduction to Open-Source AI Models with Hugging Face

The paragraph introduces the concept of open-source AI models integrated with Hugging Face. It highlights the transformative impact of Hugging Face tools on AI Builders, emphasizing the ease and speed at which one can build AI applications using a vast array of pre-trained models. The course mentioned aims to teach best practices and the experience of assembling various models, such as image recognition, language models, and speech recognition, to create innovative applications within a short time frame. The use of Hugging Face's Transformers library is emphasized, as well as the accessibility of open-source models and their weights for community benefit.

Mindmap

Keywords

💡Hugging Face

Hugging Face is a company and platform that specializes in providing tools and resources for building artificial intelligence (AI) applications, particularly in natural language processing (NLP) and machine learning (ML). In the video, Hugging Face is highlighted for its partnership and role in making open-source AI models accessible to developers. This enables AI builders to quickly deploy a wide variety of trained models, fostering innovation and expediting the development of new applications.

💡Open Source Models

Open source models refer to AI models whose architecture and weights are freely available for anyone to use, modify, and distribute. The video emphasizes the importance of these models in democratizing AI development, allowing developers to build upon existing work without starting from scratch. Examples include image recognition models, language models (LMs), and speech recognition models, all of which can be combined to create novel AI applications.

💡Transformers Library

The Transformers Library, developed by Hugging Face, is a collection of pre-trained models designed for NLP tasks but also extending to other areas such as image and audio processing. The video mentions this library as a core tool in the course, demonstrating its utility in processing text, audio, and images through an easy-to-use interface that abstracts away much of the complexity involved in handling these different data types.

💡Pipeline Object

In the context of the Hugging Face Transformers library, a pipeline object simplifies the process of applying AI models to specific tasks by handling the necessary pre-processing of inputs and post-processing of outputs. This enables developers to efficiently apply complex models to real-world problems with minimal code, as highlighted in the video for tasks like image narration and voice assistance.

💡Image Narration for the Visually Impaired

Image narration refers to the application of AI to describe the contents of an image aloud, particularly to aid those with visual impairments. The video describes a use case where an object detection model identifies objects in an image, and a text-to-speech model then narrates a summary. This exemplifies how combining different AI technologies can create accessible tools that enhance everyday life.

💡Gradio Library

Gradio is a Python library that enables developers to quickly create UIs for their machine learning models. The video highlights Gradio as a means to wrap AI applications, like an image narration assistant, in a user-friendly interface. This allows developers to make their solutions more accessible and easier for end-users to interact with.

💡Hugging Face Spaces

Hugging Face Spaces is a platform that allows developers to deploy, share, and discover machine learning applications online. In the video, Spaces is mentioned as a tool for deploying an AI-enabled image captioning service as an API, demonstrating how Hugging Face facilitates not just the development but also the distribution and usage of AI applications across the internet.

💡AI-Enabled Application

An AI-enabled application refers to any software application that incorporates artificial intelligence to perform tasks, solve problems, or provide services that would typically require human intelligence. The video focuses on teaching how to build such applications using open-source models and Hugging Face tools, covering a wide range of capabilities from image and speech recognition to natural language understanding.

💡Voice Assistant

A voice assistant is an AI-powered software that can understand and respond to voice commands, performing tasks or services for the user. The video outlines how participants will learn to build components of a voice assistant by integrating automatic speech recognition and text-to-speech models, showcasing the versatility of AI in creating interactive and responsive technologies.

💡Natural Language Tasks

Natural language tasks are challenges in processing or understanding human language that AI models, especially those trained on large language datasets, are designed to tackle. These tasks include summarizing text, translating languages, and conversing with users. The video indicates that participants will explore how to use open-source models for these tasks, underscoring the potential of AI to bridge communication gaps and enhance information accessibility.

Highlights

Introduction of open-source models integrated with Hugging Face.

Hugging Face tools have been transformative for AI Builders, enabling rapid AI application development.

The course teaches best practices for quickly deploying a variety of trained open-source models.

Learn to assemble different models such as image recognition, language models, and speech recognition into new applications.

Use of Hugging Face Transformers library for processing text, audio, and images in open-source models.

Combining models to assist individuals with visual impairments by describing images aloud.

Application of trained object detection models to identify objects within images.

Utilization of text-to-speech models to narrate a summary of images.

All models used in the course are open-source, with models and weights openly available for download.

Hugging Face's contribution to making open-source models more accessible, significantly boosting the AI community.

Instructor introduction: Unice Fod, Mark Sun, and Maria Halis, all machine learning engineers at Hugging Face.

Learning to search and select models from thousands of open-source models on the Hugging Face Hub.

Interacting with models using the pipeline object from the Hugging Face Transformers Library for simplified pre-processing and post-processing.

Wrapping AI applications like an image narration assistant inside a user-friendly interface using the Gradio library.

Deploying an AI-enabled image captioning service as an API using Hugging Face Spaces for internet accessibility.

Building components for a voice assistant by integrating automatic speech recognition and text-to-speech models.

Utilizing open-source models to perform natural language tasks such as summarizing text, translating languages, and chatting with users like a chatbot.

The course aims to provide opportunities to build AI-powered applications.

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