Behavior tuning
Fine-tune open models to follow your domain language, decisions, schemas, and edge cases.
Open-source models, tuned for specific work
Fine-tuned AI that behaves like it belongs to you.
Akane Labs helps teams adapt open-source models for specialized workflows, private deployment, and production-grade reliability.
akane.run
tuned
base_model qwen2.5-14b
adapter claims-specialist-lora
eval_pass_rate 94.8%
deployment customer-vpc
What we tune
Models for teams with sharp requirements.
Generic models are impressive. Specialized models are useful. We focus on the last 20%: consistency, structure, privacy, and the weird cases that matter to your business.
Eval-led delivery
Measure quality on golden tasks, regressions, latency, and cost before the model ships.
Private deployment
Deploy in your cloud, on dedicated GPUs, or on-prem with ownership of the artifact.
Process
Small loop. Clear artifacts.
Every engagement ends with something concrete: adapters or weights, evals, deployment instructions, and a model your team can keep improving.
01
Define the target behavior and failure modes
02
Create a clean dataset from examples, docs, traces, and expert review
03
Tune with LoRA, QLoRA, preference data, or distillation where useful
04
Package the model, evals, serving path, and handoff docs
Stack
Built on tools you can actually own.
LlamaQwenMistralGemmavLLMTGITritonAWSGCPAzureOn-premRunPod
Private by default
Bring the hard examples. We tune for those.
Best fit for repeated, high-value workflows where a general model is close, but not dependable enough.
Send a project brief