Model fine-tuning trains an existing AI model further on your own examples so it learns your terminology, format and tasks, and optimization makes models smaller, faster and cheaper to run, so you get better results at lower cost.
General-purpose models are powerful but often miss domain terminology, formats and edge cases. We fine-tune and optimise models on your data to raise accuracy, reduce hallucinations, cut inference costs and run efficiently on your chosen infrastructure.
Improve performance by customizing AI models for your specific business needs. At Prominent AI Services LLP, we deliver AI model fine-tuning end to end, from discovery and data preparation to deployment, monitoring and support, so your teams get dependable results quickly and securely.
Improve performance by customizing AI models for your specific business needs.
Ideal for: Niche Industries, Research, Enterprise SaaS. We validate each idea with a measurable baseline, iterate on features and model architecture, and harden the winning model for scale with automated testing, versioning and performance monitoring.
We fine-tune open-source models such as LLaMA, Mistral and Qwen using parameter-efficient methods (LoRA and QLoRA), and use the fine-tuning services of providers such as OpenAI and Azure OpenAI when that suits. For optimization we apply quantisation, distillation, pruning and conversion to ONNX or TensorRT, and serve models with vLLM. Work is done in Python with PyTorch and Hugging Face, on GPU infrastructure in Azure, AWS or your own servers.
Choose a fixed-scope fine-tuning project, or hire machine learning developers to join your team. Fine-tuned models often power generative AI applications and NLP solutions, and can be hosted for you through custom AI API hosting.
Is your AI too generic, slow or expensive? Share your task and a few examples, and we will recommend the right approach. Start your AI journey.
Common questions about our Model Fine-Tuning & Optimization services.
Fine-tuning helps when you need consistent formats, specialised vocabulary or lower latency and cost at scale. We test prompting first and fine-tune only when it adds measurable value.
Yes. We fine-tune models such as LLaMA and Mistral inside your own cloud account so training data stays under your control.
We apply quantisation, distillation, pruning and efficient serving frameworks to reduce model size and compute cost without unacceptable accuracy loss.
Costs cover data preparation, training compute and evaluation. Parameter-efficient fine-tuning of an open-source model is often affordable; most of the effort usually goes into preparing good examples. We quote after reviewing your data.
For style or format, a few hundred good examples can be enough. For specialised classification or extraction, a few thousand labelled examples usually give better results. Quality matters more than quantity.
You do. The model weights, training data and code are delivered to you, and fine-tuning on open-source models avoids lock-in to a single provider.
Once example data is ready, training and evaluation usually take two to four weeks. Preparing and labelling the data is often the longest step.
