Hugging Face training, 1-on-1.You ship open-source AI models.
A Hugging Face expert opens your account with you and works through what matters: navigating the Hub and picking the right model, running Transformers, fine-tuning on your data, and deploying through the Inference API. We start from your real case, not theory.
★★★★★ 4.7/5 · 300+ pros trained · France Num certified
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GeminiWe ship Hugging Face models for real clients, not just in theory.
Most Hugging Face trainings are recorded tutorials by people who opened the tool the night before. At Hack'celeration it's the opposite: finding the right model on the Hub, running it with Transformers, fine-tuning it in LoRA on client data, deploying it on Inference Endpoints and wiring it through the API, that's our daily agency work. We ship Hugging Face models for clients every week. Everything we teach you, we practice on shipped projects. We know the traps (the model under a bad license, the GPU that saturates, the fine-tuning that adds nothing over a good prompt) because we've already solved them.
- We ship Hugging Face models for clients every week, not just in theory
- 1-on-1 format: the trainer adapts to your level, Python beginner or seasoned ML
- We tell you when an open-source model isn't the right call (sometimes the OpenAI API is enough for your case)
- We start from your real dataset and your actual needs, not a dummy example
Hugging Face training rests on four concrete pillars.
Hugging Face approached badly means hours lost in Hub search, the wrong model saturating your GPU, and a fine-tuning that adds nothing. Most of the trouble comes from the method, not the tool. We pick up your real account and work through the four pillars together.
- The Hub and model selection
The right model at the right time, out of hundreds of thousands
The Hub hosts over 1 million models, plus datasets and Spaces. The beginner trap is grabbing the most-downloaded model without checking its license, size or task. We teach you to read a model card, compare leaderboards, verify the license (commercial or not) and choose between a big model and a quantized version that fits your GPU. You leave knowing how to find and decide, not wander through search.
Explore the Hub - Transformers and inference
Run a model locally, pipeline and inference
The Transformers library loads a model in a few lines. We teach you pipelines (text, image, audio), the tokenizer, CPU vs GPU, and memory handling so you don't crash your machine. We also cover when to use the hosted Inference API instead of running everything yourself. You finish with a model that actually answers, from your notebook or your Python script.
See Transformers - Fine-tuning on your data
Specialize an open-source model, on your real dataset
A generic model doesn't know your domain. We prepare your dataset (format, cleaning, train/test split), choose LoRA or QLoRA with the PEFT library to fine-tune without renting a farm of GPUs, and evaluate the result on your own examples. We tell you honestly when a good prompt is enough and when fine-tuning is worth the cost. You leave with a model trained on your data and pushed to the Hub.
See fine-tuning - Deployment and API
Put your model in production, Inference Endpoints and API
A model that works in a notebook is useless until it's reachable. We deploy through Inference Endpoints (autoscaling, managed GPU) or a Space for a Gradio demo, and we wire the call into your product through the API. We look at real cost at scale, latency and control over your data (self-hosted vs closed API). You leave with an endpoint that runs, called from your code or your no-code automation.
Talk deployment
Meet our trainers, leave with a plan.
Drop your email. We get back to you to connect you with a Hack'celeration-certified trainer: we look at your use case, your dataset and your hardware, and tell you where to start. No commitment, even if you don't take the training.
- A diagnosis of your ML case and the right model to target
- The first actions to take, in priority order
- The right 1-on-1 format for your Python level and goals
- An honest take: open-source model or closed API for your case
Your Hugging Face program, step by step.
Five steps, no skipping. Each one on your real account, with a clear deliverable. From the first session we frame your ML case and pick your first model. By the end, you use Hugging Face without us.
- Step 1 · ML use-case audit
We open your Hugging Face account and frame your real need
First session, we share your screen and look at your case together. What do you want to do: classify text, extract info, generate, transcribe audio, vision? We look at your data, your Python level, your hardware (local GPU or not) and your budget. We spot what an open-source model handles well and what isn't worth it. You leave with a clear diagnosis and the list of work items, in priority order. No theory, your real account.
- Step 2 · Pick and run a model
We find the right model on the Hub and get it running
We get concrete. We search the Hub for the model that fits your task, read its model card, check its license and size, compare two or three candidates. Then we run it with Transformers: pipeline, tokenizer, inference on your own examples. If your machine is tight, we switch to a quantized version or the Inference API. By the end of this step, you have a model that answers on your data, not on a docs example.
- Step 3 · Fine-tuning on your data
We specialize a model on your real dataset
This is where your model gets genuinely useful. We prepare your dataset (format, cleaning, train/test split), choose LoRA or QLoRA with PEFT to fine-tune without blowing your GPU budget, and launch the training. We evaluate the result on your own examples, not a generic benchmark. We compare against a plain prompt to confirm the fine-tuning actually brings something. You finish with a model trained on your data and pushed to the Hub.
- Step 4 · Deployment and API
We put your model in production and wire it up
A model in a notebook serves no one. We deploy through Inference Endpoints (managed GPU, autoscaling) for production, or a Space with Gradio for a shareable demo. We wire the call into your product through the Hugging Face API, look at latency, real cost at scale and control over your data. We also connect it to Make or n8n if your flow is no-code. You leave with an endpoint that runs, called from your code.
- Step 5 · Autonomy
You use Hugging Face without us
The number one goal: you become autonomous. By the end of the program, you know how to navigate the Hub, pick a model, run it with Transformers, fine-tune it on your data and deploy it through the API. You no longer need an agency to ship an open-source model. And if you want to delegate later, we also run a Hugging Face agency, but that's not the point here.
Why train 1-on-1 with us.
- 300+Pros already trained on AI
More than 300 people have gone through our trainings across France and Europe. Devs, data scientists, technical teams, small-business founders. Not vanity numbers: people who run and deploy open-source models for real, instead of staying stuck in the docs.
- 4.7/5Rating across 334 verified reviews
Average rating of 4.7 out of 5, across 334 reviews. We won't promise ML is magic: you have to practice on your real cases and your real data. But the 1-on-1 format makes the difference between following a tutorial you don't get and actually handling models.
- 1:1A dedicated expert, not a class of 100
You're not a number in a webinar. A trainer opens your real Hugging Face account, looks at your code and your dataset, and works through your actual cases. We schedule sessions around your availability, replays included.
A working agency, recognized by the French State.
Hack'celeration is certified Activateur France Numérique and holds the AI Ambassador title, both granted by France Num to organizations that genuinely drive the digital transformation of companies. On the ground, we ship Hugging Face models for clients every week: more than 300 pros trained and a 4.7/5 rating across 334 verified reviews, left by the people who took our programs, not just by the buyer.
- Certified Activateur France Numérique
- AI Ambassador (France Num)
- 300+ pros trained across France and Europe
- 4.7/5 across 334 verified reviews
The questions we get the most.
What is a 1-on-1 Hugging Face training?
An individual program with a Hugging Face expert, not a class of 100 people. We open your real account, look at your code and your dataset, and work through your actual cases: navigating the Hub, model selection, Transformers, fine-tuning, deployment and the API. You ask your questions live, the trainer adapts the pace to your level. We schedule sessions around your availability, and you leave with concrete actions every time. That's what makes the difference between following a tutorial you don't get and actually handling open-source models.How much does the Hugging Face training cost?
There is no single price. We connect you with a trainer certified by Hack'celeration, matched to your need and your level. It varies from one trainer to another, based on their profile and the format that fits your project.Do I need to code or know Python to take this?
A minimum of Python really helps, because Transformers, datasets and fine-tuning all go through code. But the 1-on-1 format starts from your exact level: Python beginner, we go step by step on notebooks ready to adapt; seasoned data scientist, we jump straight to fine-tuning and optimization. If you don't want to code at all, we frame an Inference API usage wired into no-code instead, but to get the most out of the Hub, a bit of Python stays the right investment.Hugging Face or the OpenAI API: which one to pick?
It depends on your case, and sometimes the answer is both. The OpenAI API (or Anthropic) is unbeatable to start fast, with no infra to manage, on general reasoning tasks. Hugging Face and open-source models win when you want control over your data, a contained cost at high volume, a specialized model fine-tuned on your domain, or self-hosting for regulatory reasons. In training, we help you decide honestly based on your volume, your budget and your privacy constraints.How much does fine-tuning cost and do I need a big GPU?
Less than you'd think, thanks to LoRA and QLoRA. These methods (via the PEFT library) train only a small fraction of the parameters, so you can fine-tune a model with several billion parameters on a single GPU rented by the hour, sometimes for a few euros per run. No need to buy a farm of cards. In 1-on-1, we pick the method that fits your model size and budget, launch the training on a cloud GPU, and check the gain justifies the cost before going further.Are open-source models as good as closed models?
For many tasks, yes, and the gap closed fast in 2025. On classification, extraction, transcription or a well-scoped domain, a good fine-tuned open-source model often rivals a closed model, while keeping your data in-house. On very complex general reasoning, big closed models still hold the edge. The real question isn't which is better in the absolute, but which is better for your task, your volume and your budget. We help you measure that on your own cases.How do you deploy a Hugging Face model in production?
Several paths depending on your need. Inference Endpoints deploys your model on a managed GPU with autoscaling, reachable through an API URL, with no infra to manage yourself. A Space with Gradio serves to publish a shareable demo. For full control, we self-host it on your own server. We look together at latency, cost at scale and availability. In training, we deploy your model on a real endpoint and wire it into your product or your automation, not just an example.Does my data stay private with Hugging Face?
That's exactly one of the big advantages of open source. If you run a model locally or on your own server, your data never leaves your infra, unlike a third-party API where you send everything to the provider. On Inference Endpoints, the compute runs on dedicated infrastructure you control. For a private dataset on the Hub, you choose its visibility (private by default). In training, we frame the architecture best suited to your GDPR constraints and the sensitivity of your data.What exactly are the Hub, Transformers and Spaces?
Three building blocks of the ecosystem. The Hub is the platform hosting over a million models, thousands of datasets and Spaces, with search, model cards and versioning. Transformers is the Python library that loads and runs these models in a few lines (text, image, audio). Spaces are hosted demo apps, often in Gradio, to show a model without deploying infra. In training, we use all three together: we find a model on the Hub, run it with Transformers, and publish a demo as a Space.Is the training online or in person?
100% online, over video, 1-on-1. You join the sessions from anywhere, we share your screen, your notebook and your Hugging Face account live. Sessions are recorded if you want to revisit them. The individual format means real interaction: you're not a number in a webinar of 100, the trainer answers your questions about your code and your dataset. That's what makes learning concrete on a topic as technical as open-source models.
Open source can do a lot for you. Meet your trainer.
Drop your email. An expert who ships Hugging Face daily looks at your case and shows you how to ship an open-source model for real. No commitment, even if you don't take the training.