Model Training & Fine-Tuning
Adapt open-source language models to your domain, your data, and your tasks — entirely within your infrastructure. From one-time fine-tuning to continuous retraining pipelines, we handle the full ML lifecycle.
Efficient Fine-Tuning (LoRA / QLoRA)
We use parameter-efficient fine-tuning techniques — LoRA and QLoRA — to adapt large language models to your domain without requiring massive GPU clusters. This makes on-premise fine-tuning practical even on modest hardware.
Domain Adaptation
Pre-trained models are adapted to your industry vocabulary, writing style, and task requirements. Legal, medical, financial, manufacturing — any domain where generic models fall short.
Evaluation & Benchmarking
Custom evaluation suites are built for your specific tasks. We define metrics that matter for your use case and track model performance before and after fine-tuning, giving you measurable proof of improvement.
Continuous Training Pipelines
As your data evolves, the model can too. We set up automated retraining pipelines that trigger on data thresholds, schedule periodic fine-tuning runs, and promote new model versions through staging to production.
Technology Stack
Training frameworks and model serving infrastructure