Updates over the previous version: updated validation outputs for the cluster-based model fixed a bug with how the PF candidates were stored added single particle gun samples to validation added new timing runs for the baseline algo, included memory information run the GNN model up to ~10k inputs added hypertuning summary tables Trained models and evaluation results for the upcoming paper "Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors", https://doi.org/10.48550/arXiv.2309.06782. The archive contains the following subfolders: clusters_best_tuned_gnn_clic_v130 MLPF GNN model configs and weight files after hypertuning the inputs are reconstructed tracks and Pandora clusters the outputs are reconstructed PF candidates trained on tt and qq v1.3.0 (1M events each) hits MLPF GNN model configs and weight files inputs are reconstructed tracks and calorimeter hits outputs are reconstructed PF candidates trained on tt, qq and gun samples (K0L, gamma, pi+-, pi0, neutron, ele, mu) v1.2.0 training was restarted several times from previous checkpoints hypertuning GNN and transformer model before and after hypertuning summary tables of the hypertuning runs timing scaling study of baseline PF with number of gun particles on CPU scaling study of GNN model with number of input elements on GPU gpu_scaling the scaling study of model training on multiple accelerator cards The training dataset is available at Pata, Joosep, Wulff, Eric, Duarte, Javier, Mokhtar, Farouk, Zhang, Mengke, Girone, Maria, & Southwick, David. (2023). Simulated datasets for detector and particle flow reconstruction: CLIC detector (1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8260741