LLM Fine-Tuning and Custom Model Development Services
Fine-tuning adapts an existing model to your task, your format and your domain language, rather than training a model from scratch. Mixcore Studio fine-tunes and evaluates open-weight models for teams that have hit a wall with general-purpose APIs — usually on cost at volume, on latency, on output consistency, or on a requirement that data never leaves their infrastructure.
We will also tell you when not to do it. Fine-tuning is frequently proposed for problems that a better prompt, a retrieval layer or ordinary code would solve faster and more cheaply. We start by establishing whether the gap is real.
When fine-tuning is the right tool
- Output format must be exact — the model has to produce a rigid structure every time, and prompting alone still drifts.
- Specialised domain language — legal, clinical, financial or industrial vocabulary a general model handles imprecisely.
- Unit cost at volume — a small fine-tuned model can match a large general one on a narrow task at a fraction of the inference cost.
- Latency limits — a smaller self-hosted model responds faster and more predictably than a frontier API under load.
- Data residency — regulation or contract requires inference to happen inside your own environment.
When it is the wrong tool
Fine-tuning does not reliably teach a model new facts, and it is a poor way to keep a system current. If the requirement is accurate answers over a changing body of documents, retrieval-augmented generation is the correct architecture and will cost far less to maintain. If the requirement is broader reasoning ability, a stronger base model beats a fine-tune of a weaker one. We say so before taking the work.
What the work involves
- Dataset construction — the part that actually determines the outcome. Curating, cleaning, deduplicating and labelling training examples that represent the real task.
- Parameter-efficient tuning — LoRA and QLoRA adapters give most of the benefit of full fine-tuning at a fraction of the compute, and keep the base model swappable.
- Distillation — using a large model to generate high-quality training data for a much smaller one that you can afford to run at scale.
- Evaluation harness — a held-out test set with task-specific scoring, so improvement is measured rather than asserted.
- Serving and deployment — self-hosted inference through vLLM with batching, quantisation and autoscaling sized to your real traffic.
The stack we work in
We work primarily with open-weight families such as Llama, Qwen and Mistral, since these can be tuned, quantised, self-hosted and audited without vendor restriction. Training runs on rented GPU capacity sized to the job. Serving typically uses vLLM for throughput. Where a hybrid makes sense, we route easy traffic to a small tuned model and escalate hard cases to a frontier API, which usually gives the best balance of cost and quality.
Small language models and on-device inference
The most significant recent shift is how capable small models have become on narrow tasks. A tuned model in the small parameter classes can now handle classification, extraction, routing and structured generation at a quality that previously required a frontier model, which changes where inference can physically run. That opens deployment targets a large model rules out: on a phone, on embedded hardware, inside a hospital or factory network, or anywhere a workload must keep functioning without a reliable connection. When data cannot leave a device at all, this stops being a cost optimisation and becomes the only viable architecture.
How an engagement runs
We begin with a baseline measurement of what a well-prompted general model already achieves on your task, because that number is what any fine-tune must beat to be worth its maintenance burden. If the gap justifies the work, we move to dataset construction, tuning and evaluation, and hand over both the model and the harness needed to keep improving it.
Our expertise
- LoRA and QLoRA tuning
- Training dataset construction
- Model distillation
- Evaluation harnesses
- Inference cost optimisation
- Self-hosted model serving
Frequently asked questions
Should we fine-tune a model or use retrieval-augmented generation?
Use retrieval when the requirement is accurate answers over a body of information that changes, because updating means re-indexing a document rather than retraining. Use fine-tuning when the requirement is a consistent format, a specialised tone or domain vocabulary, or a cheaper and faster model for one narrow task. Many production systems use both, with a tuned model reading retrieved context.
How much training data do we need to fine-tune a model?
Far less than most teams expect, provided the data is good. For narrow formatting and tone tasks, a few hundred to a few thousand high-quality examples is often enough with parameter-efficient methods such as LoRA. Quality and consistency of labelling matter considerably more than raw volume, which is why dataset construction is the bulk of the engagement.
Which models can be fine-tuned?
We work mainly with open-weight families including Llama, Qwen and Mistral, since these can be tuned, quantised, self-hosted and audited without vendor restriction. Several hosted providers also offer managed fine-tuning of their own models, which we use when the operational simplicity outweighs the loss of portability.
Will a fine-tuned model be cheaper to run?
Usually yes for a narrow, high-volume task, because a small tuned model can match a much larger general one on that specific job at a fraction of the inference cost. The saving has to be weighed against the ongoing burden of retraining, evaluating and serving your own model, which is why we measure the baseline first.
Can the model run inside our own infrastructure?
Yes. Open-weight models are served through vLLM in your own cloud account or on-premise environment, with quantisation and batching tuned to your traffic, so no prompt or response is sent to a third-party API.
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