LLM Fine-Tuning
Adapt a language model to your terminology, tasks and output formats.
How we deliver LLM Fine-Tuning
We help organisations adapt suitable language models to specialised tasks, terminology, output formats and domain-specific requirements using parameter-efficient techniques such as Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA).
The work covers dataset preparation and preprocessing, training configuration and experimentation, model evaluation and comparison, and deployment with inference optimisation.
Fine-tuning is not always the answer. We evaluate it against prompting, RAG and other alternatives, and the right approach depends on the task, data, model licence, compute budget and expected performance.
What's included
Fine-tuning open-source language models
Low-Rank Adaptation (LoRA)
Quantized Low-Rank Adaptation (QLoRA)
Dataset preparation & preprocessing
Training configuration & experimentation
Model evaluation & performance comparison
Model deployment & inference optimisation
Monitoring & ongoing improvement
LLM Fine-Tuning FAQs
Common questions about LLM Fine-Tuning at Watsbit.
1How much data do we need?
It depends on the task. Some adaptations work with a few hundred high-quality examples; others need more. We review your data first and tell you honestly whether fine-tuning is worth it.
2Can the fine-tuned model run on our own infrastructure?
Yes. Open-source models adapted with LoRA or QLoRA can be served on your cloud or on-premise hardware, subject to the model's licence.
Start your LLM Fine-Tuning project
Book a free 30-minute consultation. We will map your idea to the right services, a realistic budget and a timeline.
