AI Practice›Fine-tuning & Training

Domain-specific models that general AI cannot match.

General-purpose models plateau at domain depth. We fine-tune on your proprietary data — with proper dataset curation, PEFT/LoRA training, preference alignment, and evaluation harnesses — so your model knows your domain as well as your experts do.

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training run · epoch 4/6

Train loss: 0.847

↓ 34% from baseline · converging

Domain eval: 91.4%

+23pp vs gpt-4o on held-out set

LoRA rank: 16 · α: 32

2× A100 80GB · ETA 2h 14m

Checkpoint saved

mlflow run #a3f2 · v0.4.0-ft

What we deliver

From dataset to deployed, domain-specialized model.

Dataset curation

Collection, cleaning, deduplication, and formatting of your proprietary data into instruction-tuning or preference pairs.

JSONL · ShareGPT · Alpaca format

PEFT / LoRA training

Parameter-efficient fine-tuning that adapts the model to your domain without retraining billions of weights from scratch.

LoRA · QLoRA · DoRA · AdaLoRA

Evaluation harness

Held-out domain benchmarks, human evaluation protocols, and regression suites that track quality across model versions.

LM Eval Harness · Promptfoo · custom

DPO / preference alignment

Direct preference optimization and RLHF to steer model behavior toward your quality standards and business rules.

DPO · ORPO · KTO · RLHF

Optimized inference

Quantization, speculative decoding, and continuous batching so your fine-tuned model runs fast and cheap in production.

vLLM · Triton · GGUF · AWQ

Model version management

Experiment tracking, artifact storage, model registry, and staged rollout pipelines so you can promote with confidence.

MLflow · W&B · HuggingFace Hub

Our approach

Data quality is the training ceiling.

Data audit & prep

We assess your data for quality, coverage, and legal use. Bad data in, bad model out — we fix this before writing a line of training code.

Baseline benchmark

We measure the base model and a prompt-only approach first. Fine-tuning only makes sense if the data and use case justify the cost.

Train & iterate

LoRA training with hyperparameter search, eval-on-checkpoint, and early stopping — fast iteration cycles, tracked in MLflow.

Serve & monitor

Optimized inference serving with latency SLAs, quality monitoring, and retraining pipelines when new data arrives.

Frameworks & infrastructure we train on

HuggingFace TRLAxolotlLoRA / QLoRADPO / ORPOvLLMMLflowWeights & BiasesA100 / H100 clusters

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Ready to train a model on your proprietary data?

No SDR, no discovery-call gauntlet. A senior ML practitioner personally reviews every submission and replies within one business day.

Dataset curation & quality audit

LoRA / QLoRA PEFT training

Preference alignment (DPO / ORPO)

Optimized inference serving (vLLM)

Direct contact

Use the contact form

(202) 903-9000