Qwen
A range of open-weight models to assess for language, structured outputs, and domain adaptation. License and architecture vary by checkpoint.
Publisher resources ↗CUSTOM MODELS / BUILT AROUND YOUR WORK
Make the model
your own.
Adapt an open-weight foundation.
Or build a tiny specialist from the ground up.
Your domain, your constraints, your definition of success. We turn the right data into a model built for a specific job.
MODEL WORKSHOP
DOMAIN DATA
YOUR MODEL
HELD-OUT EVALQuality ↗Latency ↘Cost / task ↘
build.specialist(
task="your_workflow",
evaluate="before_release"
)
task="your_workflow",
evaluate="before_release"
)
DEFINE → ADAPT → EVALUATEILLUSTRATIVE
01 / MODEL CUSTOMIZATION
The right method.
For the right change.
Start with a baseline. Choose training only when it solves a measured gap.
02 / OPEN FOUNDATIONS
Start with capability.
Add your context.
We select a specific checkpoint against your task, deployment target, and permitted use.
Google Gemma
Open models for customization, subject to the Gemma Terms of Use. Select a size and modality that fit the workflow.
Publisher resources ↗Meta Llama
Open-weight foundations under Meta’s applicable Llama license. Evaluate language coverage, model size, and deployment requirements.
Publisher resources ↗Microsoft Phi / MAI
Assess open-weight Phi models and eligible MAI checkpoints. MAI availability varies; downloadable weights and training rights must be verified.
Publisher resources ↗More open-weight models
Other foundations can be assessed when their model cards, licenses, and measured task performance support the project.
Publisher resources ↗Open-weight does not mean unrestricted. We check adaptation, redistribution, commercial use, and teacher-output permissions before selecting a model. MAI-DS-R1 is one published open-weight example; it is a large model, not a sub-1B deployment recommendation.
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.
Start with a small foundation.
Fine-tune or distill into an existing compact model, subject to teacher and student permissions.
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.
04 / FROM TASK TO DELIVERY
Build the evidence.
Then ship the model.
A staged engagement with review points, reproducible artifacts, and a clear handover.
Define
Agree the task, data boundary, error costs, and acceptance criteria.
Prepare
Review data rights. Curate examples and freeze a held-out evaluation set.
Customize
Compare prompting and retrieval with the appropriate training method.
Validate
Measure task quality, failure modes, latency, and cost on target hardware.
Deliver
Package permitted weights or adapters, configuration, evaluation reports, and a deployment recipe.
05 / YOUR DEPLOYMENT, BY DESIGN
Custom model.
Considered deployment.
Choose customer-controlled infrastructure or an agreed cloud environment. Define access, retention, permitted egress, and artifact ownership in the project scope.
For local hardware and governed inference, pair model customization with INFRARENCE ↗. Model tuning alone does not provide confidential computing.
Security & data commitments ↗06 / SPECIALISTS AT WORK
One task.
A model that fits.
Examples to scope and evaluate—not promises of universal accuracy.
EXTRACTION
Documents → structured records
Learn field formats and validate against your schema.
Explore ↗CLASSIFICATIONTickets → the right queue
Use your categories, escalation rules, and review thresholds.
Explore ↗GENERATIONYour brief → your house style
Adapt structure, terminology, and tone for reviewed drafts.
Explore ↗START WITH A WELL-DEFINED JOB
What should your
model do better?
Let’s scope the build ↗No confidential data needed for the first conversation.
Technical references & model availability
Our service categories use established customization methods. This is an independent service offering, not an Amazon, Google, Meta, or Microsoft partnership or a claim of feature parity.