FYVIE

Model Tailoring

We fine-tune and train models to fit your constraints while achieving the highest intelligence density

Trained to fit your budget, not to a leaderboard

Most models are trained to win benchmarks and squeezed afterwards to fit whatever hardware they land on. Accuracy is lost at that last step, and nobody planned for where.

We work the other way round. The constraint comes first — a memory ceiling, a power envelope, a latency deadline, a part number you have already committed to — and the model is trained to reach its accuracy target inside it. Quantisation is part of training rather than a step applied to finished weights, so the model learns at the precision it will ship with.

We listen to your constraints

01

Memory

A ceiling in megabytes rather than a hope. Base model, depth and bit-width are chosen to fit under it before a single step of training runs.

02

Power

Milliwatts per inference on the part you are shipping, with the arithmetic chosen for what your silicon does cheaply.

03

Latency

A deadline in milliseconds. Context length, vocabulary and depth are traded against it up front, where the trade is still cheap.

04

Hardware

The board, accelerator or microcontroller you have already committed to — including parts with no floating-point unit at all.

05

Domain

Your corpus, your vocabulary, your formats. A small model that knows one domain well beats a large one that knows every domain badly.

06

Privacy

Training and inference can both stay inside your own infrastructure. The weights are delivered to you rather than rented back.

How a tailored model is built

  1. 01

    Scope the envelope

    We start from the part and the budget: memory, power, latency, and what accuracy would count as success. That envelope becomes the specification everything afterwards is measured against.

  2. 02

    Choose the base

    Architecture and bit-width are selected together — one bit, ternary, or a mixed scheme where the few layers that need more precision are the only ones given it.

  3. 03

    Train at the shipping precision

    Quantisation-aware training rather than a post-hoc squeeze. The model learns with the precision it will run at, so there is no final step left to lose accuracy in.

  4. 04

    Verify on the hardware

    Evaluation on your data and measurement on your target — memory held, power drawn, latency met — not on a datacentre GPU standing in for it.

  5. 05

    Hand over the weights

    You receive the weights, the evaluation and the runtime notes for your target. No inference API sits in the loop, and nothing has to phone home.

Tell us the constraint. We will train to it.

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