LLM Fine-TuningServices
Find out whether fine-tuning will actually beat better prompting or retrieval for your task, and if it will, get a tuned model with the evidence to prove it.
LLM Fine-Tuning Services: what the work involves
Fine-tuning is often proposed before anyone has tried the cheaper options. Teams collect a few hundred examples, train a model, and find that it sounds right but still gets facts wrong, or that a prompt rewrite would have done the same job. Others have a real need, such as a strict output format, a niche domain vocabulary, or low latency at high volume, but no clean dataset, no evaluation, and no plan for retraining when the base model changes.
DevKey starts with a baseline: the best prompt and retrieval setup we can build, measured on a held-out evaluation set. If a gap remains that tuning can plausibly close, we design the dataset, with labelling guidelines, deduplication and a clean train and test split, then run supervised or preference tuning, usually with parameter-efficient methods such as LoRA. We compare results against the baseline, document the data lineage, and hand over the model, training scripts and a retraining procedure.
Core features
Feasibility and baseline study
We measure strong prompting and retrieval first, so you can see exactly what gap tuning would need to close and whether it is worth chasing.
Dataset design and curation
Examples are sourced, cleaned, deduplicated and labelled against written guidelines, with sensitive data removed and a separate untouched test set.
Parameter-efficient training
LoRA and similar methods tune open models such as Llama or Mistral on modest hardware, keeping experiments cheaper and easier to repeat.
Preference tuning where useful
When quality is about style or judgement, ranked pairs can steer the model, though we only use this if the baseline shows it is needed.
Evaluation against the baseline
Task metrics, regression checks on general ability and human review samples are reported side by side with prompting and retrieval results.
Handover and retraining plan
You receive weights, configuration, scripts, a data lineage record and a procedure for refreshing the model when data or base models change.
What we get right before launch
Fine-tuning does not add reliable knowledge
It shapes behaviour and format far better than it injects facts, and facts go stale. For knowledge that changes, retrieval is usually the right tool, and we say so when that is the honest answer.
Data rights and privacy
Training data can leak back through model outputs. We confirm you hold the rights to the data, remove personal and confidential information, and test for memorisation before deployment.
Ongoing cost and maintenance
A tuned model is something you maintain: re-evaluation after base-model changes, drift monitoring and periodic retraining. We estimate these obligations up front and compare them with hosted alternatives and their vendor lock-in.
Tools and technology
- PyTorch
- Hugging Face Transformers and PEFT
- Llama and Mistral models
- OpenAI fine-tuning API
- vLLM
- MLflow
- Label Studio
- Python
- Docker
Common questions, answered
Do we need fine-tuning for a company chatbot?
Usually not. Most chatbots need good retrieval, clear instructions and guardrails. Fine-tuning becomes relevant for a strict style, a specialised format or cost reduction at scale, and we test that after building the baseline.
How much data do we need?
It depends on the task. Some narrow formatting tasks benefit from a few hundred high-quality examples, while others need thousands. Quality and coverage matter more than raw numbers, and we run small pilots to learn where returns diminish.
Can you fine-tune for Urdu or Roman Urdu?
It is possible and sometimes useful, particularly for classification and normalisation. Open models start weaker in Urdu than English, so data volume and evaluation need care, and hosted models may already be good enough.
Who owns the resulting model?
You do, subject to the licence of the base model, which we review with you. Some open models restrict certain uses, and hosted fine-tunes remain tied to the provider, so we explain these terms before choosing.
More AI Agents & Automation services
All AI Agents & Automation servicesPrivate LLM Deployment
Keep prompts and documents inside your own environment by running an open model you control, with the sizing, security and quality evidence to justify the choice.
Prompt Engineering and Evaluation
Replace trial-and-error prompting with versioned prompts and a test suite, so every change is measured and regressions are caught before users see them.
AI Data Labeling Pipelines
Get training and evaluation data that is consistent, auditable and affordable to produce, with model assistance and quality controls built into the workflow.
Ready to start your LLM Fine-Tuning Services project?
Tell us what you need and we will come back with a clear scope, timeline and the questions worth answering before any build starts.