Research
Building efficient AI,
openly.
We publish our methods, benchmarks, and findings so the research community can build on what we learn.
Areas
01
Efficient model architectures
Exploring how 1B-parameter models can match the practical performance of much larger systems through architecture choices, distillation, and targeted training.
02
Domain-specific fine-tuning
Developing principled approaches to fine-tuning general-purpose checkpoints for scientific, coding, and conversational tasks without catastrophic forgetting.
03
Evaluation & benchmarking
Designing evaluation frameworks that reflect real-world task difficulty rather than narrow benchmark saturation — and releasing them for open use.
04
Inference optimization
Quantization, pruning, and runtime techniques that make sub-2B models viable for CPU-only deployments without meaningful quality degradation.
05
Alignment at small scale
Studying how RLHF and constitutional approaches transfer to smaller models, and where smaller-scale alignment diverges from large-model behavior.
06
Open data & reproducibility
Curating and releasing the training data mixes, evaluation sets, and pre-processing pipelines that underpin Kedy's models.
Publications
Kedy Specialized Small Language Models for Scientific, Code, and General Tasks
Domain Distillation at Scale: Transferring Scientific Reasoning from Large to Small Models
Evaluation Beyond Benchmarks: Towards Task-Grounded Assessment of Small Language Models
Collaborate with us.
We're interested in working with academic researchers and institutions. If you have a project that aligns with our work, reach out.