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

Your data. Your weights. Your advantage.

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

Need a full custom LLM?