AI Fine-Tuning

Domain accuracy without training from scratch.

LoRA, QLoRA, and SFT on your proprietary corpus. The base model's general intelligence stays intact - your domain knowledge gets layered on top. Faster, cheaper, production-ready.

All services
4-6
weeks to production
LoRA
QLoRA · SFT methods
100%
adapter weight ownership
Fixed
fee, no surprises
Techniques
LoRA QLoRA SFT RLHF DPO Eval Harnesses
What we deliver

Fine-tuning that actually works in production.

Data curation & cleaning
We audit your training corpus, remove noise, balance classes, and build instruction-following datasets - the quality foundation everything else depends on.
LoRA / QLoRA adapters
Parameter-efficient fine-tuning that keeps your inference cost low. Adapters merge cleanly with the base model for a single deployable artifact.
Supervised fine-tuning (SFT)
Instruction-tuning on your curated examples to shift the model's behavior precisely toward your domain tasks and output format.
Domain eval harness
Custom benchmarks tied to your real tasks - not generic MMLU. We only ship when the model clears the accuracy bar agreed at project start.
Quantization & serving
4-bit or 8-bit quantization to fit your hardware budget. Deployment via vLLM, Ollama, or TGI with a documented inference API.
Drift monitoring
Post-launch behavioral monitoring to catch model drift before it affects production quality. Included in the 30-day support window.
Method selection

Right technique for your budget & dataset.

Method Data needed Compute cost Best for We use it when
SFT1k - 100k examplesMediumInstruction followingYou have labeled examples
LoRA500-50k examplesLowStyle & domain shiftConsumer GPU budget
QLoRA500-50k examplesVery lowLarge models on small GPULlama/Mistral on A100
DPOPreference pairsMediumPreference alignmentYou have human feedback
Fine-tuning in one paragraph

When fine-tuning is the right call

Fine-tuning is worth doing when prompting has plateaued: the model understands the task but consistently misses your domain’s vocabulary, format or judgement calls. Modulus handles dataset curation, the training run (SFT, DPO, LoRA or QLoRA), benchmark evaluation against the base model, and deployment. Where retrieval would solve the problem more cheaply, we say so before taking the work — most requests for fine-tuning are better served by a well-built RAG pipeline. You own the resulting adapter weights outright. Modulus works with teams in the United States, United Kingdom, Singapore, Hong Kong, Australia, Indonesia, Germany and France.

When should I fine-tune instead of using RAG?

Fine-tune when the model needs to learn a style, format or judgement pattern. Use retrieval when it needs access to facts that change. Most requests framed as fine-tuning problems are actually retrieval problems, and retrieval is cheaper to build and to maintain.

What fine-tuning methods do you use?

Supervised fine-tuning (SFT) for format and task adherence, DPO for preference alignment, and LoRA or QLoRA where parameter-efficient adaptation keeps training and inference costs down. The method is chosen against the task, not by default.

How much training data do I need?

Fewer examples than most teams expect — often several hundred to a few thousand high-quality pairs. Data quality and consistency matter far more than volume; a small clean set outperforms a large noisy one reliably.

Who owns the fine-tuned model?

You do. Modulus delivers the adapter weights, the training data pipeline and the evaluation harness. There is no dependency on a Modulus-hosted endpoint to keep using what was built.

How do you know the fine-tune actually improved anything?

Every engagement includes a benchmark suite built before training starts, scoring the base model and the tuned model on the same held-out set. If the tuned model does not beat the baseline on your task, that result is reported rather than hidden.

Your data. Your weights. Your advantage.

Free discovery call. Adapter weights yours. 30-day post-launch support.

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