AI, Data & Intelligence
Model Fine-Tuning
Adapting language models to your tasks, terminology and output formats with supervised fine-tuning and parameter-efficient methods such as LoRA, when prompting and retrieval are not enough.
Capability overview
What model fine-tuning involves
Fine-tuning trains an existing model further on your examples so it performs a specific task more consistently. It is the right tool when outputs must follow a precise format every time, when domain terminology confuses general models, or when a smaller, cheaper model needs to match the quality of a much larger one on a narrow task.
It is not the right tool for teaching a model facts that change, which retrieval handles better. We fine-tune hosted models through provider fine-tuning services and open-weight models using parameter-efficient methods such as LoRA and QLoRA, and always compare the result against a well-prompted base model to prove the gain.
What is included
What fine-tuning involves
Dataset curation
Training examples assembled from historical outputs and expert corrections, deduplicated, balanced and checked for personal data.
Method selection
Full supervised fine-tuning, LoRA adapters or preference tuning chosen based on data volume, budget and hosting plans.
Training runs
Experiments on cloud GPUs or provider services with hyperparameters and datasets tracked for reproducibility.
Comparative evaluation
Fine-tuned model scored against the base model and prompt-only approaches on a held-out test set.
Serving and cost analysis
Deployment options for the tuned model compared on latency and cost per request at expected volumes.
How we work
How we deliver model fine-tuning
Prompt baseline
The best achievable prompt-only result measured, which sets the bar fine-tuning must beat.
Data preparation
Examples cleaned, formatted and split into training, validation and test sets.
Training experiments
Several configurations trained and compared, watching for overfitting and loss of general ability.
Expert review
Domain experts blind-review outputs from tuned and baseline models to confirm the improvement is real.
Deployment and retraining plan
Model deployed with monitoring and a schedule for retraining as new examples accumulate.
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Questions & answers
Questions about Model Fine-Tuning
Cannot find what you need? Our team responds to technical and commercial questions within one business day.
Ask a questionFor format and style tasks, a few hundred high-quality examples can be enough. Complex classification or specialised generation often needs a few thousand. Quality and consistency of examples matter more than volume.
Not reliably. Fine-tuning shapes behaviour and style rather than storing retrievable facts. For answering from documents, retrieval-augmented generation is the better approach.
Yes, when an open-weight model is fine-tuned. Smaller models can run on a single modern GPU, keeping data and inference entirely within your environment.
Projects are fixed price per phase, covering dataset preparation, experiments and evaluation. GPU or provider training costs are estimated up front and billed at cost.
Next step
Discuss model fine-tuning with Acmez
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