optimization
Alexandria_geometry_optimization_paths_PBE_3D
Cite this dataset Schmidt, J., Hoffmann, N., Wang, H., Borlido, P., Carriço, P. J. M. A., Cerqueira, T. F. T., Botti, S., and Marques, M. A. L. Alexandria geometry optimization paths PBE 3D. ColabFit, 2024. https://doi.org/10.60732/c88da7df
This dataset has been curated and formatted for the ColabFit Exchange
This dataset is also available on the ColabFit Exchange:
https://materials.colabfit.org/id/DS_s6gf4z2hcjqy_0
Visit the ColabFit… See the full description on the dataset page: https://huggingface.co/datasets/colabfit/Alexandria_geometry_optimization_paths_PBE_3D.Alexandria_geometry_optimization_paths_PBE_2D
Cite this dataset Schmidt, J., Hoffmann, N., Wang, H., Borlido, P., Carriço, P. J. M. A., Cerqueira, T. F. T., Botti, S., and Marques, M. A. L. Alexandria geometry optimization paths PBE 2D. ColabFit, 2025. https://doi.org/10.60732/8781419f
This dataset has been curated and formatted for the ColabFit Exchange
This dataset is also available on the ColabFit Exchange:
https://materials.colabfit.org/id/DS_6pieq95jrqpn_0
Visit the ColabFit… See the full description on the dataset page: https://huggingface.co/datasets/colabfit/Alexandria_geometry_optimization_paths_PBE_2D.speculators-ci-datasets
speculator-tutorial
Raw vs. on-policy regenerated conversation data for training speculative-decoding
drafters (EAGLE-3 / DFlash / DSpark style), with the original source data kept alongside
so you can see exactly what regeneration changes and why it matters.
Prompts come from UltraChat-200k. The verifier / teacher model is Qwen/Qwen3-8B.
Why regenerate at all?
A speculative-decoding drafter is trained to predict what the verifier would say next.
If you train it… See the full description on the dataset page: https://huggingface.co/datasets/inference-optimization/speculators-ci-datasets.human_assisted_action_preference_optimization4c_optimizationThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "koch_follower",
"total_episodes": 25,
"total_frames": 8494,
"total_tasks": 1,
"total_videos": 50,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:25"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/ethanCSL/4c_optimization.synthesized-cloud-optimization-recommendations
Synthesized Cloud-Optimization Recommendations
18 scenarios that pair cloud telemetry with a hand-crafted optimization
recommendation. Use them to train models or to evaluate AI agents.
Summary
Each scenario has multi-tier telemetry, a Terraform file describing the
deployed infrastructure, and a gold-standard recommendation.
The dataset is built around a simple input-output mapping. The input is
telemetry plus the infrastructure. The output is an optimization… See the full description on the dataset page: https://huggingface.co/datasets/ameau01/synthesized-cloud-optimization-recommendations.