datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
llm-serving-selector-regret
LLM-Serving Selector Regret
LLM-Serving Selector Regret is a metrics-only research dataset for studying learned policy selection in LLM-serving schedulers. It contains derived selector/oracle/regret objects generated by Soroush Vahidi's research workflow, not raw request traces.
Creator / Provider
Dataset creator/provider: Soroush Vahidi.
The released selector/regret and policy-suitability metrics were generated by Soroush Vahidi's research workflow. Underlying… See the full description on the dataset page: https://huggingface.co/datasets/SoroushVahidi/llm-serving-selector-regret.llm-serving-scheduler-baselines
LLM-Serving Scheduler Baselines: Simulation Performance Outcomes for Scheduler Policies
This is a comprehensive, text-free, highly structured simulation results dataset for large language model (LLM) serving schedulers. It contains policy-level outcome records generated across synthetic scheduler stress tests and an added TraceLab-derived out-of-distribution policy sweep. The dataset compares 12 highly optimized third-party baseline schedulers against APT-Serve (a… See the full description on the dataset page: https://huggingface.co/datasets/SoroushVahidi/llm-serving-scheduler-baselines.