LLM fine-tuning and evaluation
We fine-tune LLMs and build evaluation pipelines to reach domain-level accuracy. From dataset strategy to regression testing, we ensure your models stay reliable over time.
What we deliver
- Dataset design and labeling strategy
- Supervised fine-tuning and distillation
- Automated evals and regression testing
Architecture approach
We define success metrics early, then iterate on data, training, and evaluation until the model meets production thresholds.
Stack and tooling
- Hugging Face and PyTorch
- Evaluation harnesses and benchmarks
- Model hosting in cloud or on-prem
Outcomes
- Higher precision for domain tasks
- Consistent outputs with guardrails
- Lower token costs with smaller models