The model knows everything. It just does not know your business.
Fine-tuning closes that gap. We take the right foundation model and adapt it to your domain, your data, and your accuracy requirements.
Live training run
Loss drops. Cursor reaches convergence. The fine-tuned model lights.
The problem
General is not the same as good.
A frontier language model knows a lot. It knows the internet, it knows code, it knows how to write a cover letter. What it does not know is your contract structure, your support history, your product catalog, or the precise way your domain uses language. On a general task it performs well. On your specific task it halves.
Fine-tuning changes that. You take a model that is already good at language and adapt it to your domain using your own data. The result is a model smaller than the frontier one, faster, cheaper to run, and more accurate on the exact task you care about. Not because it is smarter in general, but because it has seen your data.
Most teams skip fine-tuning because it sounds expensive. LoRA and QLoRA changed that calculus. You can fine-tune a capable model on a consumer GPU in hours, not weeks. The barrier is not compute. It is knowing what to fine-tune on, how to evaluate whether it worked, and how to serve it afterward. That is the engineering.
Here is the version you might recognize. You wrote a support reply classifier on top of a frontier model. The prompt is now four pages long. It has fifteen examples glued in, a list of every edge case anyone has ever hit, and a paragraph that begins "do NOT, under any circumstances." It works most of the time. Then a customer phrases a refund request a little sideways and the model files it under billing, and you add example sixteen. You are not prompting anymore. You are hand-coding a decision tree in English, paying frontier prices per call to run it, and watching latency climb with every line you add. The pattern you keep correcting by hand is exactly the pattern a fine-tune would learn once and apply for free.
The part nobody warns you about
Teach it one thing and it can forget the rest.
A model has a fixed budget of weights. When you fine-tune, you are not adding a new room to the house. You are repainting the rooms that are already there. Push hard on your narrow task and the model gets better at it while quietly getting worse at everything else: reasoning, instruction following, the careful tone it used to have. The field has a name for this. Catastrophic forgetting. Most teams never see it coming because they only test the thing they trained, and the thing they trained looks great.
This is the trap under the "small model beats the giant" headline. Yes, your fine-tuned model can win on the one task. The question nobody asks is what it lost to get there. A classifier that learned your refund categories but can no longer explain its decision, or follow a follow-up instruction, is not the win it looks like on the leaderboard.
LoRA and QLoRA help here, and not by accident. They freeze the original weights and train a small set of new adapter matrices alongside them. The base knowledge stays intact underneath, so the damage is limited by design. This is one of the real reasons we reach for parameter-efficient methods first, not just to save GPU hours. But it is a guardrail, not a force field. Crank the adapter rank too high, train too many epochs on too few examples, and a small model will still overfit and forget. Cheap to run does not mean safe to ignore.
So we do not only measure whether the fine-tuned model got better at your task. We hold back a set of general checks the base model already passed and run them again after training. If the new model aced your task but lost the ability to do the ordinary things you still need, that is not a model you ship. That is a regression with good marketing. The hard part of fine-tuning was never the training. It is knowing, with evidence, that you traded up and not sideways.
How we build it
The right base, adapted on your data.
We pick the base model that fits the task, not the one with the best benchmark score. These are the patterns we work with.
Base model selection
Llama, Mistral, Phi, Qwen, Gemma. The right base model depends on your task, your latency requirements, and your licensing constraints. We do not default to the largest one. We pick the one that fine-tunes well on your data volume and runs within your budget. Size is a real lever here: a 3B model that fits on one modest GPU and answers in milliseconds can be the right answer over a 70B model that needs a cluster and a longer wait. We also read the license before we fall in love with a model, because some weights ship with terms that quietly restrict commercial use, and that is a problem you want to find at the start, not after you have shipped.
LoRA and QLoRA
We use parameter-efficient fine-tuning so you get domain adaptation without retraining every weight. Smaller compute, faster iteration, same result for most tasks. When you need deeper adaptation we know when full fine-tuning is worth the cost. The mechanics matter: instead of moving billions of weights, LoRA freezes the base model and trains a pair of small low-rank matrices that ride alongside it, so a run that would have needed a server farm fits on hardware you already own. QLoRA goes further and loads the frozen base in 4-bit precision, which is how a model that would not otherwise fit in memory suddenly does. Fewer knobs to train also means fewer ways to break the model you started with.
Dataset curation
The model is only as good as what you fine-tune on. We help you identify what data you already have, how to clean and format it, and how much is actually enough. More is not always better; the right examples matter more than volume. A few hundred clean, correct, consistently formatted examples will routinely beat tens of thousands of noisy ones, and on a small model there is no slack to absorb bad data: a contradictory label does not average out, it gets learned. So we hunt for the wrong answers, the duplicates, and the examples that disagree with each other before a single training step runs. Garbage in does not get smoothed over. It gets memorized.
Evaluation before and after
We measure your baseline on the fine-tuning task before we start, and compare after. If the fine-tuned model does not beat the base model on your task, we investigate why before we ship anything. We hold out a test set the model never trains on, so the score reflects real generalization and not memorization of examples it has already seen. And we do not only score the target task: we re-run a set of general checks the base model passed, to catch what the fine-tune may have eroded on the way to winning. A model that improved on paper and regressed in practice is the most expensive kind of mistake, because you do not notice it until it is in front of a user.
Serving the fine-tuned model
A fine-tuned model that lives on a researcher's machine is not a product. We package it for inference: quantized for your latency target, containerized, with an API your application can call. We fold the adapter back into the base weights so there is no extra hop at runtime, then quantize to the precision your latency budget allows and load-test it against real traffic, not a single happy-path prompt. The output is something your application calls like any other service, with a known cost per request and a known response time, not a notebook somebody has to babysit.
Distillation when you need it smaller
Sometimes the best teacher for your small model is a bigger model. Distillation uses a large, capable model to generate or grade training examples, and you fine-tune a compact student model to reproduce that behavior. You get most of the quality of the big model at a fraction of the size, latency, and cost to run, and the student is yours to host privately. This is how you take a task that only a frontier model handles today and move it onto hardware you control, without paying frontier prices on every single call forever.
"A fine-tuned small model that knows your domain will outperform a frontier model that is guessing from context. The cost difference is not a bonus, it is the point."
What you get
A model that knows your domain.
Not a frontier model with a long system prompt crossed and hoped. Adapted weights, measured improvement, and the pipeline to retrain when your data grows.
- Base model selection with justification for your task and constraints
- Fine-tuned model weights adapted to your domain data
- Training and evaluation code you can rerun when you have more data
- Evaluation report comparing base model vs fine-tuned on your actual task
- Inference-ready package: quantized, containerized, with an API endpoint
- Documentation so your team can add data and retrain without us
Not sure whether fine-tuning or prompt engineering is the right move? Tell us the task and we will give you the honest answer.
Invoke usIs this the right call
When this fits.
Good fit
- You are prompting a frontier model and it keeps getting the domain-specific details wrong
- You have examples of correct input-output pairs, even if not many
- Your task is specific enough that a general model is overkill
- The per-token cost of a frontier API is becoming a real line item
Wrong call
- You do not have any task-specific data to fine-tune on. Prompt engineering is the right starting point.
- The task changes so frequently that a fine-tuned model would be stale in weeks
- You want the model to know things it has never seen before. Fine-tuning teaches style and format, not new facts. That is a RAG problem.
Deployment and scale
Smaller, faster, yours to run.
A fine-tuned model does not need to live behind a third-party API. It runs on your infrastructure, in your environment, with your data never leaving. If compliance requires it, the whole stack runs on-prem. If you want managed hosting, we package it for that too.
We quantize to the precision your latency target allows. A 4-bit quantized model runs on hardware you already have and costs a fraction of the full-precision version to serve. The accuracy difference on your specific task is usually negligible.
When your data changes, the model needs to catch up. We build the retraining setup so your team can add new examples and trigger a fine-tuning run without starting from scratch or calling us back.
What we settle before we begin: what the model needs to do, what data you have to train on, and where it is allowed to run. Everything else follows from those three.
Tell us what the frontier model keeps getting wrong.
Tell us the task, the inputs, the expected outputs, and where the current model falls short. We will tell you whether fine-tuning is the right call, and what the training setup should look like.