Model fine-tuning
A model trained on how your business works.
LoRA and QLoRA fine-tuning on open models, or hosted fine-tuning on OpenAI, Vertex AI and Bedrock. Built from your data, measured against a baseline, deployed where you need it.
Typical timeline: 4–8 weeks
Capabilities
What's included
- Dataset preparation: cleaning, de-duplication, labelling and splits
- LoRA and QLoRA fine-tuning on Llama, Qwen and Mistral
- Hosted fine-tuning on OpenAI, Vertex AI or Amazon Bedrock
- Before-and-after evals against the base model
- Self-hosted serving (vLLM or similar) or managed endpoints
- Training notes, configs and a repeatable retraining pipeline
How we work
From brief to live
- 01
Assess
3–5 daysYour data and task, and a test of whether prompting or retrieval would be enough.
- 02
Prepare
1–2 weeksDataset built, reviewed and split, with a baseline eval score.
- 03
Train
1–3 weeksFine-tuning runs compared on the eval set until the gains are clear.
- 04
Deploy
1 weekServing on your infrastructure or a managed endpoint, with monitoring.
FAQ
Good to know
Often a few hundred to a few thousand good examples. Quality matters more than volume, and we can help create or label them.
Open models when you need control, privacy or lower serving cost at scale. Hosted fine-tuning when you want speed and no infrastructure to run.
You do. Adapters, weights and datasets are delivered to your accounts, subject to the base model's licence.
Have something in mind?
Send a short brief or book a 20-minute call. We reply within one working day.