01FT
Format & style models
Models that write in your report structure, coding conventions or house voice every time.
LLM.co · Private AI & Custom AI DevelopmentCustom AI model fine-tuning
Open-weight models adapted to your terminology, document formats and tone, then distilled to run on smaller, cheaper hardware. You keep the weights.
FIG. — IBM 7094, NASA Ames, 1966LLM.co
Fine-tuning is not always the answer. Often better retrieval or a better prompt gets there first, and we will tell you when that is the case. It earns its keep when the job needs a consistent format, specialist vocabulary, or a small model fast enough to run at the edge.
Training runs on your hardware or in your cloud account. The training data never leaves your control and the resulting weights are delivered to your storage.
| Methods | LoRA, QLoRA, full fine-tuning, distillation, preference tuning |
|---|---|
| Base models | Llama, Qwen, Mistral, Gemma, Phi |
| Data | Stays in your environment; curated with your reviewers |
| Output | Weights, training code and evaluation reports, delivered to you |
| Serving | Quantized for your GPUs, served via vLLM |
What we build
What this looks like in practice.
02FT
Domain vocabulary
Adaptation to clinical, legal, engineering or financial language the base model handles poorly.
03FT
Classifiers & extractors
Small, fast models for routing, tagging and field extraction at high volume.
04FT
Distillation
A large model's behavior compressed into a smaller one that fits a single GPU or edge box.
LLM fine-tuning services for open-weight models
Fine-tuning adjusts a pretrained model's weights using examples from your own work. The result is a model that writes in your formats, understands your terminology, or performs one narrow task faster and more cheaply than a large general model. Our LLM fine-tuning services cover data curation, training, evaluation, compression and serving.
LLM.co fine-tunes open-weight models such as Llama, Qwen, Mistral, Gemma and Phi as part of its custom AI development practice. Training runs on your hardware or in your cloud account, so the work stays private AI from start to finish.
When fine-tuning is worth it, and when it is not
We start every engagement by testing cheaper options. Better prompts and better retrieval solve many problems without training anything. Fine-tuning earns its place in a few clear situations.
- Output must follow a strict structure or house style every time.
- The base model handles your clinical, legal, engineering or financial vocabulary poorly.
- A high-volume task such as routing or field extraction needs a small, fast model.
- A model must run on a single GPU or an edge device with no network.
Method choices: LoRA, full fine-tuning and distillation
LoRA and QLoRA train a small set of adapter weights on top of a frozen base model. They need less hardware, train quickly and make it easy to keep several adapters for different tasks. Full fine-tuning updates every weight and suits larger shifts in behavior, at a higher compute cost.
Distillation trains a smaller model to copy a larger one's outputs on your tasks. It is often the best route to cheap, fast inference on modest hardware. Preference tuning can follow any of these to align tone and judgment with reviewer feedback. We train candidates across base models and compare them side by side on held-out cases.
Data, risk and quality control
Training data comes from your own records and stays in your environment. We clean and deduplicate it, label examples with your reviewers, and handle sensitive fields by written policy before training. Held-out test cases never enter the training set.
The main risks are overfitting, loss of general ability and memorized sensitive data. We check each against the evaluation set and targeted probes before release. If a tuned model does not beat the baseline on your scores, we say so and do not ship it.
When you compare LLM fine-tuning services, ask who holds the training data during the run, whether you receive the weights, and how the vendor proves the tuned model beats a prompting baseline. Clear answers to all three are a good sign.
What you receive
You receive the trained weights or adapters, training code, the curated dataset and its documentation, evaluation reports, and a quantized build served through vLLM on your GPUs. Everything is delivered to your repositories and storage. There is no license back to us, and your team can retrain the model as your data changes.
How it works
Four steps, each one reviewed.
01
Prove the need
Baseline prompting and retrieval first. Fine-tune only where the evaluation set says it will pay.
02
Curate data
Clean, deduplicate and label examples from your own records, with sensitive fields handled by policy.
03
Train & compare
LoRA or full fine-tunes across candidate base models, scored side by side on held-out cases.
04
Quantize & serve
Compress for your hardware and serve behind the same gateway as everything else.
Questions
Common questions.
What do LLM fine-tuning services include?
Proving the need against a prompting and retrieval baseline, curating and labeling training data, training candidate models, side-by-side evaluation on held-out cases, quantization for your hardware, and serving behind your gateway. You receive the weights, training code, dataset documentation and evaluation reports.
How much data do we need to fine-tune a model?
For format and style, a few hundred to a few thousand good examples is often enough. Vocabulary and knowledge-heavy work needs more, and some of that is better solved with retrieval. We size the dataset during discovery and tell you if your records are enough to justify training.
Should we fine-tune or use RAG?
Use retrieval when answers depend on facts in documents that change. Use fine-tuning when you need a consistent format, specialist vocabulary or a small fast model. Many systems use both: retrieval for the facts and a tuned model for the structure. We test both against your evaluation set before deciding.
How long does a fine-tuning project take?
Discovery takes two weeks and includes the baseline test. A focused first system, including data curation, training, hardening and deployment, usually reaches production in eight to twelve weeks. Data cleanup and reviewer labeling time are the biggest variables.
How much does it cost to fine-tune an LLM?
Discovery is a fixed fee, and later phases are quoted one at a time. Cost depends on the amount of data curation and labeling, the training method, the size of the base model and the compute used. LoRA on a smaller model costs far less than full fine-tuning of a large one.
Who owns the trained model?
You do. The weights, adapters, training code and evaluation sets are delivered to your repositories and storage. There is no license back to LLM.co. Open-weight base models carry their own license terms, which we review with you when choosing the base model.
Is our training data kept private?
Yes. Training runs on your hardware or in your own cloud account, and the data never leaves your control. Sensitive fields are handled by a written policy before training, and we test the tuned model for memorized sensitive content before release.
Can this run as private AI on our own infrastructure?
Yes. Fine-tuned models are quantized for your GPUs and served on hardware you own, in your own cloud account, or on an air-gapped network. Nothing calls a third-party model API unless you decide it may.
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