Model Fine-Tuning
Off-the-shelf models are generalists. We adapt them to your domain, data, and tasks — whether that is a large language model, an image or vision model, a classification model, an embedding model, or a speech model — so you get measurable accuracy gains, lower cost, and consistent behaviour in production.
What's included
- Fine-tuning for LLMs, vision & multimodal models
- Image classification & object-detection model training
- Custom embedding & retrieval models
- Dataset curation, cleaning & synthetic augmentation
- LoRA / QLoRA & full fine-tuning pipelines
- Eval harnesses with domain-specific benchmarks
Outcomes
- Higher accuracy on your real-world tasks than a generic model
- Smaller, cheaper models that punch above their size
- Consistent tone, format, and behaviour you can rely on
- A repeatable training pipeline you own
Our model fine-tuning process
Data audit & curation
We assess your data, clean it, label gaps, and generate synthetic examples where needed.
Train & experiment
We run LoRA/QLoRA or full fine-tunes across model families and track every experiment.
Evaluate
Domain-specific eval harnesses prove the tuned model beats the baseline before it ships.
Deploy & monitor
We package the model as an endpoint, wire up monitoring, and keep improving it on fresh data.
What you get
- Fine-tuned model weights and/or a hosted inference endpoint
- Reproducible training & data-prep pipeline
- Evaluation report with before/after benchmarks
- Handover documentation
Great for
Other services
Let's build something intelligent.
Tell us about your product and goals. We'll come back with a pragmatic, build-ready plan — usually within one business day.