HUGGING FACE TRAINING: MASTER AI WITHOUT STARTING FROM SCRATCH
Hack'celeration offers a Hugging Face training to learn how to use the largest open-source machine learning platform. Want to integrate AI into your projects without spending months training models? That's exactly what we'll cover together.
Concretely, we'll explore the Models Hub to find and use pre-trained models, master the Transformers library and pipelines, do fine-tuning to adapt models to your needs, deploy demos with Spaces and Gradio, and connect the Inference API to your applications.
Whether you're a developer discovering ML, a data scientist wanting to accelerate your projects, or a product team wanting to add AI without building everything from scratch — this training is for you.
Our approach: 100% practical. No theoretical slides about the history of transformers. You code, you test, you deploy. At the end, you're autonomous to use Hugging Face in your real projects.
Start learning for free.
Why take a Hugging Face training?
Because Hugging Face has democratized access to AI. Instead of spending weeks training models, you can use state-of-the-art models in just a few lines of code.
The problem? The platform is massive. Over 500,000 models, dozens of libraries, dense technical documentation. Without a method, you waste time searching for the right model, struggle with tokenizers, and don't know how to go from "it works locally" to "it runs in prod".
Here's what you'll master:
- Navigate the Models Hub: You'll know how to find the right model for your use case (NLP, vision, audio), understand metrics, and avoid abandoned models.
- Use Transformers and pipelines: You'll learn to load a model, run it with pipelines, and understand tokenizers to adapt inputs.
- Fine-tune a model: You'll adapt a pre-trained model to your own data with the Trainer API, without needing a GPU cluster.
- Deploy with Spaces and the Inference API: You'll create demos with Gradio and Spaces, and connect the API to your applications.
- Manage datasets: You'll use the Datasets library to load, transform, and prepare your data efficiently with tools like Airtable.
Whether you're starting from scratch or have already tinkered with Hugging Face, we give you the right reflexes to use the platform efficiently and avoid common pitfalls.
What you'll learn in our Hugging Face training
MODULE 1: DISCOVER THE HUGGING FACE ECOSYSTEM
We start by understanding how Hugging Face is organized. Not just "it's a model platform", but really how everything fits together.
You'll explore the Models Hub: how to search for a model, read a model card, understand metrics (downloads, likes, benchmarks), and identify if a model is maintained or abandoned.
We also see the different libraries: Transformers for language and vision models, Datasets for data, Diffusers for image generation, and how they integrate together.
You create your account, configure your environment (access tokens, local cache), and run your first model in a few lines.
At the end of this module, you know how to navigate the ecosystem and you've run an NLP model and a vision model.
MODULE 2: MASTER TRANSFORMERS AND PIPELINES
We dive into the heart of Hugging Face: the Transformers library. It's what allows you to use all these models easily.
You'll understand pipelines: these abstractions that let you do classification, NER, translation, Q&A, text generation... in one line of code. We see how to use them, configure them, and understand their limits.
We then look under the hood: tokenizers. You learn how text is transformed into tokens, why it's important, and how to handle special cases (long texts, special languages).
You also discover how to load a model manually with AutoModel and AutoTokenizer, to have more control when pipelines aren't enough.
At the end of this module, you know how to use pipelines for 90% of cases, and you understand how to go further when needed.
MODULE 3: WORK WITH DATASETS
Models are great, but without clean data, you can't do anything. We see how to use the Datasets library to manage your data efficiently.
You learn to load datasets from the Hub (there are thousands), but especially to load your own data: CSV, JSON, local files, databases.
We see transformations: filter, map, shuffle, split into train/test. All optimized (lazy loading, cache, Arrow format) to handle millions of rows without exploding your RAM.
You also discover how to prepare your data for fine-tuning: batch tokenization, padding, truncation, and creating DataLoaders compatible with PyTorch.
At the end of this module, you know how to prepare any dataset for training or inference, properly and efficiently.
MODULE 4: FINE-TUNE A MODEL
This is where it gets powerful. You take a pre-trained model and adapt it to your specific use case.
We use the Hugging Face Trainer API: you configure your TrainingArguments (learning rate, batch size, epochs), load your model and data, and launch training.
You learn best practices: how to choose a base model, how much data you need, how to avoid overfitting, and how to evaluate your model with the right metrics.
We also see options for training without a local GPU: Google Colab, Hugging Face AutoTrain, and cloud solutions. Because not everyone has an RTX 4090 under their desk.
You do a complete fine-tuning: text classification on your own categories. At the end of this module, you have a custom model running that you can reuse.
MODULE 5: DEPLOY WITH SPACES AND THE INFERENCE API
A model running locally is good. A model accessible in prod is better. We see how to deploy with the Hugging Face ecosystem.
You create a Space with Gradio: a web interface to test your model, shareable with one click. We see how to customize the interface, handle inputs/outputs, and optimize performance.
We explore the Inference API: you connect a model directly to your application via REST API. We see authentication, rate limits, and how to handle errors.
You also discover Inference Endpoints for dedicated deployment: when you need more control, dedicated GPU, or guaranteed latency.
At the end of this module, you have a live demo and you know how to connect Hugging Face to any application.
MODULE 6: PRACTICAL CASES AND INTEGRATIONS
We put everything together with concrete projects adapted to real use cases.
Case 1: Create a support ticket classification system. You fine-tune a model on your categories, deploy an API, connect to your tool (Make, n8n, or direct).
Case 2: Build a semantic search engine. You use embeddings and Sentence Transformers to search by meaning, not just keywords.
Case 3: Integrate text generation. You connect an LLM via the Inference API to your automation workflow.
We also see integrations with LangChain, how to combine multiple models, and best practices for going to production (monitoring, versioning, fallbacks).
At the end of this module, you have functional projects and a method to tackle any AI use case with Hugging Face.
Why train with Hack'celeration?
AN EXPERT AGENCY THAT USES HUGGING FACE FOR ITS CLIENTS DAILY
