depth pruning
removing whole layers
Model specialization
Your task does not need a 100B parameter model.
It needs one designed for it.
We start from an open model, remove what your use case does not use and specialize it with your data. The result is a smaller, faster model, and it is yours. Served on European infrastructure or deployed on your own.
We look at your case and tell you whether it pays off. Including when it does not.
the problem
You pay for capability you never use
Most of the tasks teams put into production are narrow: classifying tickets, extracting fields from a document, routing conversations, summarising case files. A generalist model carries capability for thousands of different tasks inside it. Yours uses a fraction, and you pay for the whole thing on every request: in cost, in latency and in energy.
A good prompt reduces errors. It does not reduce the size of the model running underneath.
what it is (and what it is not)
More than fine-tuning
Fine-tuning teaches your task to a model. Specialization goes one step further: it modifies the architecture of the model to remove what your task does not use.
We work with structural techniques on open models:
width pruning
reducing the internal dimension of the GLU layers
distillation
of knowledge
fine-tuning
with LoRA
First we decide what is surplus, using your data as the guide. Then we recover the general capabilities the cut affects. Finally, we specialize with your task.
The saving in cost and latency does not come from the fine-tuning. It comes from the structural part: a model with fewer parameters runs less computation on every token it generates.
Nor is it an experimental technique. These are the same families of techniques Nvidia uses to derive its Minitron family and Mistral its Ministral family: small models created from large ones through pruning and distillation.
the process
How we do it
- 01
Use-case analysis.
We start with your data: real examples of the task and, where it exists, current traffic. We define what "it works" means with concrete metrics and we work out the cost per task of what you run today. The output is an honest answer: it pays off, or it does not.
- 02
Base model selection.
We pick the open starting model based on the task, the language and the licence. It is not always the biggest or the newest: it is the one that best balances starting capability against the cost of cutting it down.
- 03
Data-guided rearchitecture.
Your data calibrates which parts of the model work on your task and which do not. With that signal we apply depth pruning and width pruning. No cut is accepted without being measured.
- 04
Recovery and specialization.
Pruning damages the general capabilities of the model. We recover them with distillation and training over a broad dataset, and then we specialize with your data using LoRA. The order matters: recover first, specialize after.
- 05
Evaluation and delivery.
The benchmark is your task, not a generic exam. We compare quality, latency and cost per task against the base model and against your current solution, and we hand you the report with the full methodology.
what you take away
The model is yours
The output of the project is a model, not a subscription. The weights are yours and the contract says so. To serve it you have two routes, and you can move from one to the other whenever you want.
On our infrastructure.
We serve it as an OpenAI-compatible endpoint on the Helmcode cloud: European infrastructure, zero logs, on a flat-rate plan or a dedicated instance. You change the base URL and your code keeps working.
On yours.
We hand you the weights and the deployment guide. This is where specialization changes the equation: self-hosting a 70B model is an infrastructure project; self-hosting your specialized model fits on a GPU you probably already have.
A model behind a closed API is not something you can take with you. This one is.
data and compliance
Your data, your jurisdiction
Training and serving happen on European infrastructure. Your data is processed under a DPA, it does not leave the EU and it is not reused for anything other than your model. At inference, zero logs: we do not store prompts or responses.
next step
Start with the diagnosis
Tell us the task, the rough volume and what you use to solve it today. We analyse it and tell you whether specialization pays off for you, which base model we would start from and what we would measure to prove it. If it does not pay off, we tell you that too and you save yourself the project.
Request a diagnosis// faq
Frequently asked questions.
What teams ask before starting a specialization project.
Who owns the model weights?
You do. The contract says so. You can serve it with us, take it to your own infrastructure or change provider whenever you want.
How is this different from fine-tuning?
Fine-tuning is the last phase of the process, not the process. Before it we modify the architecture of the model (layer and width pruning) to remove capability your task does not use. The reduction in cost and latency comes from that structural part.
Which base models do you work with?
Open models: Llama, Qwen and Gemma, among others. The choice depends on the task, the language and the licence, and it is part of the diagnosis.
What data do you need and what happens to it?
Real examples of your task. How much is needed depends on the case and we assess it in the diagnosis. Processing happens on European infrastructure, under a DPA, and your data is not reused for anything other than your model.
What if a better base model comes out next month?
The pipeline runs again on the new base. What lasts is your dataset and your evaluation suite: they work just as well with the next model. Changing base is a much smaller project than the first one.
Can I serve the model outside Helmcode?
Yes. We hand you the weights and a deployment guide. If you would rather not operate infrastructure, we serve it as an OpenAI-compatible endpoint.
Does it work for any task?
No. It works well on bounded tasks, with clear metrics and enough volume to amortise the project. If your task changes constantly or you have no representative data, it probably does not pay off. The diagnosis exists to answer that question about your specific case.
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