Open source
Konic Uno-1
Konic Uno-1 is our open-weight model family — compact, production-optimized language models for enterprise AI teams to inspect, fine-tune, and self-host on-prem today. It is the fastest way to evaluate Konic optimization methods on your own hardware.
Why Uno-1
01
Built for evaluation-first adoption.
Uno-1 exists so enterprise AI teams can measure Konic models against their incumbent on real workloads before any commercial conversation. Run it in vLLM, MLX, or llama.cpp — fully on-prem.
02
Optimization methods included.
Each release carries the same compression recipes we publish in research — pruning, distillation alignment, and INT4 quantization — so the artifact is production-shaped, not a raw checkpoint.
03
Ecosystem reachable.
Weights and model cards live on Hugging Face under konic-labs. Issues and reproductions accepted on GitHub — every published number is reproducible.
Deployment
Runs on infrastructure you own — versioned, auditable, and inside your compliance boundary.
On-prem, your stack, your pace.
Download and serve on your own machines — no data leaves your environment, no API dependency.
Upgrade path to licensed families.
When a workload needs more capability or support, Duo-1 and Tres-1 drop into the same integration patterns.
Artifacts
Uno-1 FAQ
Common questions.
Konic Uno-1 is our open-weight model family — compact, production-optimized language models for enterprise AI teams to inspect, fine-tune, and self-host on-prem today. It is the fastest way to evaluate Konic optimization methods on your own hardware.
Open weights, free to download and self-host. Commercial terms per model licence.
Yes. Konic enterprise families are engineered for on-prem deployment on infrastructure you control — on-premise, private cloud VPC, edge, or air-gapped environments. Versioned releases keep your integration stable across upgrades.
Choose one enterprise AI flow already running in production, deploy the family inside your environment, and A/B test it against the model you use today — or against your success criteria if there is no incumbent. Decide on measured results, not claims.