Start with a small foundation.
Fine-tune or distill into an existing compact model, subject to teacher and student permissions.
CUSTOMIZATION SERVICES
A model built
for your task.
From a targeted adapter to a purpose-built tiny language model. Choose the method by evidence, not by fashion.
01 / MODEL CUSTOMIZATION
The right method.
For the right change.
Start with a baseline. Choose training only when it solves a measured gap.
03 / PURPOSE-BUILT TINY MODELS
Less than 1B.
Focused by design.
A smaller model. A deliberately narrower job.
For classification, routing, structured extraction, and bounded generation, we assess whether a compact specialist can meet your quality bar with a smaller deployment footprint.
Train from the ground up.
A scoped research engagement: tokenizer, architecture, permitted corpus, pretraining, post-training, and evaluation. First validate data sufficiency and compute cost.
EXPLORE THE FOOTPRINTWEIGHTS ONLY
parameters / illustrative target
FP16 weights700 MB
Parameter count × 2 bytes, in decimal MB. Excludes runtime overhead, KV cache, activations, gradients, and optimizer state. This is not a speed or quality benchmark.
SMALLER IS A DESIGN CHOICE.
Quality still has to be earned.
Quality still has to be earned.