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How To Train And Publish Your Own Llm With Hugging Face Part 1

Leo Migdal
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how to train and publish your own llm with hugging face part 1

If you’ve been following the rise of AI, you’ve probably heard of Hugging Face — the platform that has become the home of modern machine learning models. But if you’re new to this world, it can feel overwhelming: How do you train your own AI model? What’s a dataset? And how do you actually get your model online so others can use it? This is the first post in our 3-part series: “How to Train and Publish Your Own LLM with Hugging Face.” In this post, we’ll take the very first steps — setting up Hugging Face,... By the end, you’ll have a simple example running locally — your very first step toward training an LLM!

Think of Hugging Face as the GitHub of AI models.It has: In this series, we’ll focus on training & publishing models. Hugging Face’s Transformers training library is a fantastic tool, particularly once you’re well-versed in training metrics. Once you start using it and master it, you’ll find you won’t need any other AI training tools. Now, let’s take a detailed look at the Transformers library, the training resources it utilizes, and its various parameters. Afterward, we’ll train a base LLM model, create our own LLM, and upload it to Hugging Face.

While reading this article, you can also experiment with the sample training code I’ve provided. With this code, you can download a model from Hugging Face and train it on a suitable dataset (with Instruction, Input, and Output columns). In the first step, let’s take a look at the following libraries, which you will frequently encounter and need during training: It’s a framework that offers ready-to-use architectures and high-level training/fine-tuning functions for popular and modern large language models (GPT, BERT, etc.). It supports both PyTorch and TensorFlow backends and is commonly used for tasks such as text classification, question answering, text generation, translation, and summarization. It’s a library that allows you to easily load, manage, transform, and share datasets in various formats (CSV, JSON, text files, etc.).

Thanks to its design optimized for distributed and parallel processing, it can comfortably handle even very large datasets containing millions of rows. Additionally, you can organize and preprocess your datasets with functions like map, filter, shuffle, and train_test_split. 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. Ready to dive into the LLM project using Hugging Face ? here's step-by-step guide of using Hugging Face. First, you'll need to install necessary libraries. Open your terminal or Jupyter notebook and run:

Hugging Face's Model Hub hosts thousands of pre-trained models. For beginner's, lets start with a simple text generation model like GPT-2. Now model is loaded, let's generate some text! Provide prompt and the model will do the rest. As you can see the model continue the story based on your prompt. Play around with different prompts and parameters like max_length to see how output changes.

To get started in the world of artificial intelligence in JavaScript, you should explore the TensorFlow.js library. It provides tools for creating and training models directly in the browser. To work with text, you can study natural language processing (NLP) methods. For example, transformers such as BERT are powerful models for text processing tasks. Start by learning the basic concepts of machine learning and deep learning, then move on to practical examples with TensorFlow.js. Powered by Discourse, best viewed with JavaScript enabled

and get access to the augmented documentation experience Welcome to the Hugging Face course! This introduction will guide you through setting up a working environment. If you’re just starting the course, we recommend you first take a look at Chapter 1, then come back and set up your environment so you can try the code yourself. All the libraries that we’ll be using in this course are available as Python packages, so here we’ll show you how to set up a Python environment and install the specific libraries you’ll need. We’ll cover two ways of setting up your working environment, using a Colab notebook or a Python virtual environment.

Feel free to choose the one that resonates with you the most. For beginners, we strongly recommend that you get started by using a Colab notebook. Note that we will not be covering the Windows system. If you’re running on Windows, we recommend following along using a Colab notebook. If you’re using a Linux distribution or macOS, you can use either approach described here. Welcome to the final part of our series on training and publishing your own Large Language Model with Hugging Face!

🎉 👉 If you’re landing here directly, I recommend first checking out the earlier posts: In this post, we’ll cover the most exciting part: publishing your model to Hugging Face Hub and sharing it with others. By the end, your model will be online, accessible to others, and even usable in apps! 🌍 First, install the CLI if you haven’t already:

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