Model Fine-Tuning
Domain-specific fine-tuning pipelines with LoRA, QLoRA, and full-parameter adaptation. Custom eval harnesses and automated regression testing.
LoRA · QLoRA · DPO
Modelence builds precision fine-tuning pipelines, lightweight LLM architectures, and inference systems engineered for maximum efficiency. Pure mathematics. Zero noise.
We believe the future of AI is not bigger models — it is better engineering.
Modelence was founded in 2026 with a singular conviction: the most powerful AI systems emerge not from scale alone, but from surgical precision in architecture design, weight optimization, and deployment engineering.
Our lab operates at the intersection of theoretical machine learning and production-grade systems engineering. We treat every parameter, every gradient, and every inference cycle as a first-class engineering problem.
From custom fine-tuning pipelines to sub-billion-parameter architectures that outperform models ten times their size — we build the infrastructure that makes intelligence deployable, efficient, and reliable.
Domain-specific fine-tuning pipelines with LoRA, QLoRA, and full-parameter adaptation. Custom eval harnesses and automated regression testing.
LoRA · QLoRA · DPOCustom transformer variants, mixture-of-experts routing, and sub-billion-parameter models designed for edge and cloud deployment.
MoE · SSM · HybridQuantization, KV-cache optimization, speculative decoding, and custom CUDA kernels for production inference at scale.
INT4 · Flash · SpeculativeRigorous evaluation frameworks with custom benchmarks, adversarial testing, and continuous model quality monitoring in production.
MMLU · Custom · A/BMulti-GPU and multi-node training orchestration with gradient checkpointing, pipeline parallelism, and fault-tolerant recovery.
FSDP · TP · PPEnd-to-end model lifecycle management — from experiment tracking and versioning to automated deployment and rollback pipelines.
CI/CD · Registry · MonitorLive representation of our model weight architecture — sparse activations, layer connectivity, and gradient flow patterns.
We partner with teams building production AI systems. Tell us about your engineering challenge.