Llama training, 1-on-1.Meta's LLMs, in-house.
A Llama expert opens your use cases with you and fixes what matters: pick the right Meta open-weight model, run it locally with Ollama, fine-tune it on your data, and self-host it for privacy. We start from your real tasks and your hardware, not theory.
★★★★★ 4.7/5 · 300+ pros trained · France Num certified
ActiveCampaign
Adalo
AdCreative.ai
Ahref
Airtable
Allo (The Mobile First Company)
Anthropic
Apify
Apollo.io
AttioAttio Implementation Partner
Base44
Baserow
Brevo
Bright Data
Browse AI
Bubble
CaptainData
ChatGPT
Claude
Claude CodeClaude CoworkClaude Design
Clay
Clickup
Cursor
DeepSeek
Dust
ElevenLabs
Fillout
Flutterflow
Folk CRM
Folk Implementation Partner
Freepik Spaces
Gamma
GeminiWe deploy Llama in client stacks, not just in theory.
Most Llama trainings are recorded tutorials by people who spun up the model the night before. At Hack'celeration it's the opposite: running Llama locally with Ollama, serving it with vLLM, fine-tuning it on a client's data, self-hosting it for companies whose data can't leave, that's our daily agency work. Everything we teach you, we practice on production stacks. We know the traps (the model too big for your GPU, the quantization that degrades more than expected) because we've already solved them.
- We deploy Llama in client stacks every week, not just in theory
- 1-on-1 format: the trainer adapts to your level, from prompt beginner to seasoned dev
- We tell you when Llama isn't the right call (sometimes GPT or Claude earns its price)
- We start from your real use cases and your hardware, not a dummy example
Four pillars so Llama actually runs in-house.
A badly used Llama means the wrong model on every task, a machine that crawls, and data sent where it shouldn't go. Most of the trouble comes from the choices around the model, not the model itself. We pick up your real use cases and work through the four pillars together.
- The Llama models
From 8B to 405B, and when to go open-weight
Llama is Meta's family of open-weight models: the weights are downloadable, so you can run the model in-house, fine-tune it on your data, and deploy it with no per-token cost. We map each size to a real use, from 1B and 8B for on-device and fast tasks up to 70B and 405B for demanding cases. And we're honest: a closed model like GPT or Claude is sometimes still better. We help you know when open weights genuinely win.
Pick my model - Run local and cloud
Run Llama, on your laptop or a cloud GPU
We start by getting it running. Locally, Ollama spins up a model in one command, with the right quantization to fit your machine. To serve multiple users or scale up, we move to vLLM or TGI on a cloud GPU. The API surface is OpenAI-compatible: going from dev (Ollama) to production (vLLM) is a base-URL change, not a code rewrite.
See how to run it - Fine-tuning on your data
Specialize Llama, on your domain and your tone
A base Llama is a generalist. Fine-tuned on your data, it speaks your domain, your vocabulary, your tone. We use LoRA and QLoRA (PEFT, so no need to retrain the whole model) with tools like Unsloth or Hugging Face TRL to train on a single decent GPU. We prep your dataset, run the training, export to GGUF for Ollama. You get a model that fits your use, not one more generic model.
See the fine-tuning - Self-hosting and privacy
Host it in-house, your data never leaves
Because the weights are open, you can self-host Llama on your own infra: your prompts and data never go to a third-party API. That matters for compliance, privacy, and cost (zero per-token bill once the server is up). We walk through the hardware you need, the inference server, and how to plug Llama into your stack alongside your other tools. We also say when a managed API stays simpler and cheaper for your volume.
Plan my setup
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 cases, spot where Llama can run in-house without losing quality, and tell you where to start. No commitment, even if you don't take the training.
- A diagnosis of your tasks and the hardware you have
- The first tasks to hand to Llama, in priority order
- The right 1-on-1 format for your level and your stack
- An honest take: Llama or a closed model for your case
Your Llama program, step by step.
Five steps, no skipping. Each one on your real use cases, with a clear deliverable. From the first session we map your tasks and your constraints. By the end, you run Llama on your work without us.
- Step 1 · Use case and constraints audit
We map your real need and your hardware
First session, we look at what you expect from a model: chat, code, extraction, classification, summarization. We check your real constraints: data privacy, budget, and the hardware you have (a laptop, a GPU, a cloud instance). We're honest about where Llama wins against a closed API and where it doesn't. You leave with a clear list of the tasks to hand to Llama, in priority order, and the right model for each. No theory, your real case.
- Step 2 · We get it running
Llama runs locally, then we prep for scale
We pick the right model and its quantization, and get it running on your machine with Ollama, often in minutes on a decent box. We test your prompts on it to confirm quality holds on your tasks. If you need to serve multiple users, we prep vLLM or TGI on a cloud GPU. Because the surface is OpenAI-compatible, your dev code will work in production without a rewrite. By the end, you have a Llama that answers on your real tasks.
- Step 3 · Fine-tuning
We specialize Llama on your data
If the base model isn't enough, we fine-tune it on your data. We prep your dataset (this is the real work), run a LoRA or QLoRA training with Unsloth or Hugging Face TRL to fit on a single GPU, and evaluate the resulting model on your cases. We export to GGUF to plug it back into Ollama. You practice on your own dataset, not a toy example. You finish with a Llama that speaks your domain and your tone. This step is optional if the base model already does the job.
- Step 4 · Deployment and self-hosting
We self-host Llama if your data can't leave
If your prompts can't go to a third-party API (sensitive data, compliance), we move to self-hosted on your infra. We walk through the realistic hardware you need, the inference server (vLLM, TGI), and the quality trade-offs of lighter quantized versions. We plug it into your stack so it lives next to your other tools, with a stable API for your app. We're honest about the cost of a GPU box and about cases where a managed API stays simpler and cheaper.
- Step 5 · Autonomy
You run Llama on your work without us
The number one goal: you become autonomous. By the end of the program, you know how to pick the right Llama model, run it local or cloud, fine-tune it on your data, and self-host it if needed. You no longer need an agency to run an open-weight LLM in-house. And if you want to delegate a bigger build later, we also run a Llama 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, founders, data and ops teams. Not vanity numbers: people who run Llama on real tasks and cut their AI bill, instead of paying top dollar for a job an open-weight model handles just fine.
- 4.7/5Rating across 334 verified reviews
Average rating of 4.7 out of 5, across 334 reviews. We won't pretend Llama beats every model everywhere: on some nuanced writing, GPT or Claude stay ahead. But the 1-on-1 format makes the difference in knowing exactly when Meta's open weights are the right call.
- 1:1A dedicated expert, not a class of 100
You're not a number in a webinar. A trainer opens your real use cases, looks at your stack and your hardware, and works through your actual tasks. 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 deploy Llama in client stacks 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 Llama training?
An individual program with a Llama expert, not a class of 100 people. We open your real use cases, look at your stack and your hardware, and work through your actual tasks: pick the right model, run it locally with Ollama, fine-tune it on your data, serve it with vLLM, and self-host it if needed. 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 the difference between watching a tutorial and actually running Llama in your work.How much does the Llama 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.Llama, ChatGPT or Claude: which one to use?
It depends on the task and your constraints. Llama is open-weight: you run it in-house, your data stays private, and you don't pay per token once your server is up. ChatGPT and Claude keep the edge on some nuanced writing, very long context, and the polish of their ecosystem (tools, vision, turnkey integrations). The right move is rarely one model for everything: we help you route to Llama what benefits from staying internal and cutting cost, and keep a closed model for the jobs that earn it. We're honest about where each one wins.Do I need a GPU to run Llama?
Not always. The small Llama models (1B, 3B, 8B) run on a good laptop via Ollama, quantized, at a comfortable speed for dev and prototyping, sometimes even on CPU with patience. For bigger models (70B) or to serve multiple users fast, you need a GPU with enough memory, or an on-demand cloud instance. In the training, we look at your real hardware and pick the model size and quantization that actually run on your machine, without selling you a box you don't need.How much does it cost to self-host Llama?
It depends on the model and the volume. Self-hosting removes the per-token bill: once the server is up, you only pay for hardware and electricity. For a small model locally, the cost is near zero beyond your machine. To serve a 70B continuously, count a real GPU box or a cloud instance, which can still come out far cheaper than an API at high volume. We cost out your case during the training and tell you honestly the break-even point where self-hosting beats a managed API.Can you fine-tune Llama on your own data?
Yes, it's one of the big advantages of open weights. We fine-tune Llama on your data so it speaks your domain, your vocabulary and your tone. We use LoRA and QLoRA (PEFT: we only train a small slice of the weights) with tools like Unsloth or Hugging Face TRL, which means training on a single decent GPU instead of a cluster. The real work is preparing a good dataset, and we help you there. We then export to GGUF to plug your model back into Ollama. We're honest: sometimes a good prompt or RAG is enough and fine-tuning isn't needed.Does the Llama license allow commercial use?
Yes in the vast majority of cases. Llama is released under Meta's community license, which allows commercial use, including self-hosting. The main limit targets very large platforms (above a high monthly-active-user threshold), which have to request a separate license. For a small business, an SMB or a product at launch, you're well within bounds. In the training, we point you to the exact clause for your case, but we're not lawyers: for massive or ambiguous use, we recommend a legal review.Is Llama safe with my data?
Yes, if you self-host it, and that's the whole point. By running Llama on your own infra (locally or on your private cloud), your prompts and data never go to a third-party API: nothing is sent to Meta or anyone else. That's the number one argument against a closed API when you have sensitive data or compliance constraints. If you go through a third-party cloud host serving Llama, that provider's usual rules apply. In the training, we lay out both options honestly and help you choose based on your real sensitivity.Do I need to be technical to take the Llama training?
Not for everything. Running Llama through Ollama or a chat interface takes almost no code, and we can start there. To serve with vLLM, fine-tune or self-host properly, a bit of technical comfort helps, but the 1-on-1 format starts from your exact level: beginner, we go step by step through the local run; seasoned dev, we jump straight to fine-tuning, the inference server and deployment. You learn exactly the layer you need, no more.Is the training online or in person?
100% online, over video, 1-on-1. You join the sessions from anywhere, we share your screen and your real use cases 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 stack and your hardware. That's what makes learning concrete on an ecosystem that moves as fast as Llama's.
Llama deserves to run in-house. Meet your trainer.
Drop your email. An expert who ships Llama daily looks at your use cases and shows you how to run it in-house without losing quality. No commitment, even if you don't take the training.