Github Evo2mind Huggingface Course The Hugging Face Course On

Leo Migdal

-Nov 18, 2025, 2:44 AM

github evo2mind huggingface course the hugging face course on

This repo contains the content that's used to create the Hugging Face course. The course teaches you about applying Transformers to various tasks in natural language processing and beyond. Along the way, you'll learn how to use the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub. It's completely free and open-source! As part of our mission to democratise machine learning, we'd love to have the course available in many more languages! Please follow the steps below if you'd like to help translate the course into your language 🙏.

To get started, navigate to the Issues page of this repo and check if anyone else has opened an issue for your language. If not, open a new issue by selecting the Translation template from the New issue button. Once an issue is created, post a comment to indicate which chapters you'd like to work on and we'll add your name to the list. Since it can be difficult to discuss translation details quickly over GitHub issues, we have created dedicated channels for each language on our Discord server. If you'd like to join, follow the instructions at this channel 👉: https://discord.gg/JfAtkvEtRb There was an error while loading.

Please reload this page. This is a practical course on using the Hugging Face ecosystem for machine learning. You'll learn how to use the Hugging Face Hub, debug and troubleshoot machine learning models, and build interactive demos with Gradio. This course provides a practical, hands-on approach to working with the Hugging Face ecosystem for machine learning. The Hugging Face ecosystem has become the standard for machine learning practitioners working with transformer models and beyond. Learning these tools offers several advantages:

Before starting, ensure you have the following: This course is designed to be followed along with hands-on coding. You can choose from several approaches: Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users. Contact GitHub support about this user’s behavior.

Learn more about reporting abuse. R-In (Remote Insider) is Remote Errors Journal, Ticketing and Analysis Open Source Project Management based on Scrum Workflow Engine with Dynamic Flow and Database Centric capabilities This repo contains the content that's used to create the Hugging Face course. The course teaches you about applying Transformers to various tasks in natural language processing and beyond.

Along the way, you'll learn how to use the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as the Hugging Face Hub. It's completely free and open-source! As part of our mission to democratise machine learning, we'd love to have the course available in many more languages! Please follow the steps below if you'd like to help translate the course into your language 🙏. To get started, navigate to the Issues page of this repo and check if anyone else has opened an issue for your language. If not, open a new issue by selecting the Translation template from the New issue button.

Once an issue is created, post a comment to indicate which chapters you'd like to work on and we'll add your name to the list. Since it can be difficult to discuss translation details quickly over GitHub issues, we have created dedicated channels for each language on our Discord server. If you'd like to join, follow the instructions at this channel 👉: https://discord.gg/JfAtkvEtRb and get access to the augmented documentation experience This course will teach you about large language models (LLMs) and natural language processing (NLP) using libraries from the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as... We’ll also cover libraries outside the Hugging Face ecosystem.

These are amazing contributions to the AI community and incredibly useful tools. While this course was originally focused on NLP (Natural Language Processing), it has evolved to emphasize Large Language Models (LLMs), which represent the latest advancement in the field. Throughout this course, you’ll learn about both traditional NLP concepts and cutting-edge LLM techniques, as understanding the foundations of NLP is crucial for working effectively with LLMs. This free course will take you on a journey, from classical robotics to modern learning-based approaches, in understanding, implementing, and applying machine learning techniques to real robotic systems. This course is based on the Robot Learning Tutorial, which is a comprehensive guide to robot learning for researchers and practitioners. Here, we are attempting to distill the tutorial into a more accessible format for the community.

This first unit will help you onboard. You’ll see the course syllabus and learning objectives, understand the structure and prerequisites, meet the team behind the course, learn about LeRobot and the surrounding Huggnig Face ecosystem, and explore the community resources that... This course bridges theory and practice in Robotics! It's designed for students interested in understanding how machine learning is transforming robotics. Whether you're new to robotics or looking to understand learning-based approaches, this course will guide you step by step. Across the course you will study classical robotics foundations and modern learning‑based approaches, learn to use LeRobot, work with real robotics datasets, and implement state‑of‑the‑art algorithms.

The emphasis is on practical skills you can apply to real robotic systems. This is the repository for a community-led course on Computer Vision. Over 60 contributors from the Hugging Face Computer Vision community have worked together on the content for this course. The result you have in front of you is as diverse as the community. A typical educational course is created by a small group of people, who try to match the tone of each other closely. We took a different road.

While following a plan on which content we wanted to include, all authors had freedom in the choice of their style. Other members of the community reviewed the content and approved or made change suggestions. The outcome is a truly unique course and proof of what a strong open-source community can achieve. If you want to contribute content or suggest some typo/bug fixes, head over to the Contribution Guidelines. If you are curious about the Hugging Face Computer Vision Community, read on 🔽 Welcome to a beginner-friendly, well-structured set of notes for the Hugging Face LLM Course!

🎉 These notes guide you from zero to advanced in Natural Language Processing (NLP) and Large Language Models (LLMs) using the Hugging Face ecosystem. Perfect for newcomers and enthusiasts alike! 💡 Feel free to open issues or submit pull requests to improve the notes! Join the Hugging Face Discord for community support 💬. This repository is licensed under the MIT License.

Github Evo2mind Huggingface Course The Hugging Face Course On

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