Get startedModels · Artifacts and foundations
Choose and inspect models
Choose a ready specialist model or an off-the-shelf vision-language foundation, then inspect the exact runtime and version.
Source reviewed 2026-09-16. Availability depends on your installation, permissions, and compatible runtimes. A supported path is not a guarantee of model quality or production readiness.
Outcome
Select the right starting artifact instead of training every model from scratch.
Start here
What you need to know first
Check the inputs below before starting. If you are new, begin with the first-project guide. A dataset holds media and labels; a model produces results; a deployment makes a selected model version callable. Creating one does not create the others.
Bring these inputs
- — Pretrained or trained model artifacts
- — Model card metadata and evaluation evidence
Expected output and limits
- — Version-addressable registry entries
- — Workflow, test, fine-tuning, and deployment bindings
Core workflow
Choose the capability first
Start from the output you need: detection, segmentation, classification, or vision-language. The catalog separates ready specialist versions from off-the-shelf VLM foundations so availability is never mistaken for runtime readiness.
Inspect the model card
Read names as family + scale + variant. Review provider, task, provenance, evaluation evidence, runtime profile, and version history as separate fields. Satori 2B Grounded is the Score-managed grounded foundation; Qwen3-VL 8B Instruct is available with a compatible runtime connection.
Select a version
Bind a specific artifact to a workflow, test session, or fine-tuning run.
Promote with evidence
Use evaluation and conformity results to decide which version can enter production.
What this surface supports
Available public and authorized private models
Curated specialist model library
Satori and Qwen3-VL foundation selection
Rich model cards
Version and deployment lineage
Expert section
Contracts, signals, and failure modes
Use this section when you are defining acceptance criteria, automating the surface, or reviewing whether its output is safe to promote downstream.
Quality and operating signals
- — Benchmark quality
- — Latency, memory, and model size
- — Artifact and license provenance
Common failure modes
- — Comparing metrics from different benchmarks
- — Using an unpinned latest version
- — Ignoring runtime or license constraints
Expert release checklist
□ Inputs and dependencies are pinned to immutable versions.
□ Acceptance metrics include critical classes and operating slices.
□ Failure, retry, cost, and rollback behavior are understood.
□ The resulting artifact has an owner and a downstream review path.
Engineering safeguards
- — Artifacts are versioned
- — Evaluations identify their benchmark
- — Production eligibility remains visible