FABRYKA AI / RESEARCH
Questions worth
measuring.
Three connected programmes, from learning with limited compute to measuring the systems we deploy. Current artifacts and planned studies are identified separately.
FAB-SLM-001 / Small Language Models
Scaling data mixtures and distillation for compact language models
Research programme · public model artifacts available
- Research question
- How do data composition, tokenization and teacher supervision affect model quality under a fixed parameter and training-compute budget?
- Study design
- Compare matched training runs with fixed evaluation splits; vary one factor at a time; record tokenizer, data revisions, tokens, seeds and compute. Report per-task results and limitations alongside aggregate metrics.
- Scope & compute
- Start with sub-150M models. Studies at 1B and 10B parameters are roadmap directions, subject to compute and data readiness.
- Outputs & next steps
- Published model repositories provide starting artifacts; the formal scaling study and its technical report are planned.
FAB-SYS-001 / Efficient Inference
Characterizing LLM inference on commodity GPUs
Research programme · formal comparative study planned
- Research question
- How do memory bandwidth, attention implementations, quantization and decoding strategies affect latency, throughput, memory use and output quality?
- Study design
- Compare the same model and workload across documented hardware and software configurations. Measure prefill, time to first token, decoding speed and latency distributions under specified concurrency. Evaluate quality separately from serving speed.
- Scope & compute
- Quantization, speculative decoding including DFlash, routing and commodity GPUs. Each result must state context length, batch size and load conditions.
- Outputs & next steps
- Production serving and token-probability tools support experiments. A controlled hardware comparison and technical report are planned.
FAB-DATA-001 / Polish AI Evaluation & Data
Polish data and reproducible AI evaluation
Research programme · public dataset and evaluation infrastructure
- Research question
- How do dataset composition and evaluation design affect our estimates of model capability in Polish, including document and OCR tasks?
- Study design
- Document data provenance, licensing, splits and scoring. Check contamination and reference quality. Use task-appropriate metrics and matched evaluation protocols; distinguish our measurements from externally reported results.
- Scope & compute
- Polish DynaWord, CodeSOTA benchmarks, Polish LLM evaluations and OCR evaluation methodologies.
- Outputs & next steps
- Polish DynaWord and CodeSOTA are public. Further evaluation studies and methodology reports form the research agenda.
PUBLICATION STANDARD
From hypothesis
to evidence.
Our intended reporting format connects a research question, hypothesis, baseline, method, experiments, results and limitations. Reports should identify authors, dates, funding and compute sources, and link the artifacts needed to inspect or reproduce the work.
Programme identifiers organize this research agenda. Completed experiments and published reports are listed only when a corresponding artifact is available.
Browse current outputs →