datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.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_optimizationsynthesized-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.speculators_benchmarks_tool_callAlexandria_geometry_optimization_paths_PBE_1D
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 1D. ColabFit, 2025. https://doi.org/10.60732/12246d46
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_xnio123pebli_0
Visit the ColabFit… See the full description on the dataset page: https://huggingface.co/datasets/colabfit/Alexandria_geometry_optimization_paths_PBE_1D.dflash-code-multilingual-teacher-responses-qwen235b
Code + Multilingual Teacher Responses (Qwen3-235B-A22B-Instruct-2507)
This repo now contains 302,800 total samples across the main blended
data.jsonl / .parquet file plus a second Nemotron-only file
(nemotron_code_teacher_responses.jsonl / .parquet). All responses were
generated by Qwen3-235B-A22B-Instruct-2507 in non-thinking mode
(enable_thinking=false) to match downstream speculator training and eval.
Built in two batches: an initial 59,506-row batch (50K code + 9.5K… See the full description on the dataset page: https://huggingface.co/datasets/inference-optimization/dflash-code-multilingual-teacher-responses-qwen235b.Qwen3.5-0.8B-responsesmanifest-digital-identity-optimization
Manifest of Digital Identity Optimization (DIO) & Ontology of Digital Identity (ODI) — Hugging Face Distribution Layer
Version / Verze: 1.0.3 (Hugging Face Distribution Layer)
Author / Autor: Daniel Beránek
Date of public articulation / Datum veřejné artikulace: 2026-07-26
Primary public node / Primární veřejný uzel: https://danielberanek.cz/manifest-dio/
Canonical archival record / Kanonický archivní záznam: Zenodo, DOI: https://doi.org/10.5281/zenodo.21610934
License /… See the full description on the dataset page: https://huggingface.co/datasets/danielberanek/manifest-digital-identity-optimization.nl-optimization-instantiation-metrics
Natural-Language Optimization Instantiation Metrics (v1.2)
This dataset is a text-free metrics release for a retrieval-assisted natural-language optimization instantiation pipeline. It contains per-example and aggregate evaluation outcomes for a frozen pipeline evaluated on the NLP4LP benchmark, the OptMath external validation domain (added in v1.1), and, as of v1.2, a 13-method schema-retrieval/grounding diagnostic suite (per-query breakdowns, bottleneck taxonomy, threshold… See the full description on the dataset page: https://huggingface.co/datasets/SoroushVahidi/nl-optimization-instantiation-metrics.AI-Code-Optimization-for-Sustainability-Dataset
AI Code Optimization for Sustainability: Dataset
Refactoring Python Code for Energy-Efficiency using Qwen3: Dataset based on HumanEval, MBPP, and Mercury
📄 Read the Paper | Zenodo Mirror | DOI: 10.5281/zenodo.18377893 | About the author
This dataset is a part of a Master thesis research internship investigating the use of LLMs to optimize Python code for energy efficiency.
The research was conducted as part of the Greenify My Code (GMC) project at the Netherlands Organisation for… See the full description on the dataset page: https://huggingface.co/datasets/BambusControl/AI-Code-Optimization-for-Sustainability-Dataset.LLMs-For-Optimization-ReformulationsSWE-bench_MultilingualQwen3.5-4B-responseshealth-optimization-bench-sample
Health Optimization Bench (Sample)
A 30-task public sample of Health Optimization Bench,
a rubric-graded benchmark measuring how well frontier language models handle current clinical
evidence in preventive and optimization medicine. Three tasks from each of the benchmark's ten
micro benches.
The full benchmark is 977 authored tasks with 346 released across ten micro benches. On the
current leaderboard no model scores above 71 of 100 and the field spans 66 points. Rankings:… See the full description on the dataset page: https://huggingface.co/datasets/Arcophos/health-optimization-bench-sample.Longbench_Samples_Specdecrepro-learning-rate-annealing-improves-tuning-robustness-in-stochastic-optimization-traces
Agent traces
Agent sessions published from a Trackio Logbook.
prism-o-optimization-depth
PRISM-O campaign dataset — optimization depth and the stated-actual gap
Complete, reproducible record of the PRISM-O measurement campaign: a
pre-registered instrument in which LLM operators classify organizational
improvement interventions from public filings onto a four-rung
optimization-depth ladder (D4 economics → D3 organization → D2 process →
D1 product/value) and estimate the stated-actual optimization gap against
cross-family operator noise floors.
Contents… See the full description on the dataset page: https://huggingface.co/datasets/spectralbranding/prism-o-optimization-depth.summarization_optimizationAihub Document summarization dataquantum-optimization
Neura Parse — Quantum Optimization, Annealing & Finance: QAOA, Adiabatic Methods & the Advantage Question
A research-plus-practitioner vertical on quantum approaches to combinatorial and continuous optimization and their most-piloted enterprise use cases. Covers QAOA theory and variants, adiabatic/annealing methods and D-Wave, QUBO/Ising encodings, amplitude-estimation Monte Carlo for finance, and the rigorous question of whether and where quantum beats classical (including… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-optimization.CARMO-UltraFeedbackPortfolio-Optimizationrepro-a-tight-theory-of-error-feedback-algorithms-in-distributed-optimization-traces
Agent traces
Agent sessions published from a Trackio Logbook.
repro-on-regret-bounds-of-thompson-sampling-for-bayesian-optimization-traces
Agent traces
Agent sessions published from a Trackio Logbook.
repro-flat-minima-and-generalization-insights-from-stochastic-convex-optimization-traces
Agent traces
Agent sessions published from a Trackio Logbook.
manufacturing-cost-optimization-2026Q3
Manufacturing Quarterly Cost Optimization Dataset (2026Q3)
Unified quarterly cost-analysis dataset for the manufacturing group, merged from
the China / Japan / India factory datasets hosted on Hugging Face.
Contents
11,100 records (>= 10,000) covering 9 plants across 3 regions.
Source datasets:
toolathon123/manufacturing-cn-energy-2026Q3 — China energy & raw material (4,200 rows)
toolathon123/manufacturing-jp-maintenance-2026Q3 — Japan maintenance & downtime (3… See the full description on the dataset page: https://huggingface.co/datasets/toolathon123/manufacturing-cost-optimization-2026Q3.repro-optimal-regret-for-policy-optimization-in-contextual-bandits-traces
Agent traces
Agent sessions published from a Trackio Logbook.
optimization-benchmark-datasetPaper available on arXiv
laguna-xs-ultrachat-responses