Sython AI Services

LLM Fine-Tuning Services

Our fine-tuning practice turns domain data into model behavior that can be measured and released safely. We support supervised fine-tuning, preference alignment, provider-managed API tuning, and LoRA/QLoRA workflows for open or self-hosted stacks.

Where latency or cost favors a narrower model, we compare full-size baselines with distilled or small-model options under the same evaluation gates.

Training objective design

Define supervised, preference optimization (DPO), and instruction objectives tied to specific business tasks and quality thresholds.

Data curation and formatting

Build high-signal datasets, balance class/task coverage, and enforce annotation quality for robust generalization.

LoRA, QLoRA, and small-model paths

Use parameter-efficient tuning on open or self-hosted stacks to control infrastructure costs; when evaluation supports it, target SLM footprints or distilled students so serving cost and tail latency match SLOs.

Safety and release gating

Run safety checks and benchmark deltas before deployment to prevent regressions and policy drift.

Frameworks and stack options

We adapt architecture to your constraints, model family, and compliance profile.

Hugging Face PEFT Axolotl Unsloth OpenAI Fine-tuning API Weights & Biases

Fine-tuning pipeline checkpoints

Baseline model benchmark and failure analysis.
Dataset assembly with train/validation/test segregation.
Hyperparameter and adapter strategy selection (SFT/LoRA/QLoRA).
Post-train evaluation and rollout recommendation with fallback plan.

Example: compliance answer quality improvement

A regulated team needed answer consistency on internal policy questions. We built a curated instruction dataset, tuned with QLoRA, and validated against a policy-focused golden set. Accuracy rose while inference cost remained bounded.

Frequently asked questions

How much data do we need to start fine-tuning?

Many teams can start with a few thousand high-quality examples. Data quality and coverage usually matter more than raw volume.

Should we fine-tune closed models or open models?

It depends on control, compliance, and hosting constraints. We often benchmark both paths and choose based on measurable quality-cost tradeoffs.

When do we use provider fine-tuning APIs versus adapter training?

Provider APIs are a strong fit when the vendor supports your model family, data handling terms match policy, and you want managed training without GPU operations. Adapter workflows (LoRA/QLoRA) fit when you need weights you control, custom evaluation loops, or self-hosted inference. We often combine approaches across environments after a short feasibility review.

Ready to improve your LLM stack?

We help teams move from demos to measurable business outcomes with robust quality, latency, and cost controls.

Book an LLM architecture call Get a fine-tuning feasibility review

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