datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.manifest-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.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.Meta_Plan_Optimization
MPO Datasets
This folder contains the datasets for the MPO experiments.
Paper: https://hf.co/papers/2503.02682
Code: https://github.com/WeiminXiong/MPO
File Structure
alfworld_metaplan_preference_pairs.json: includes comparison data for the DPO optimization phase of the ALFWorld meta planner.
sciworld_metaplan_preference_pairs.json: includes comparison data for the DPO optimization phase of the SciWorld meta planner.
alfworld_metaplan_sft.json: includes the metaplan data… See the full description on the dataset page: https://huggingface.co/datasets/xwm/Meta_Plan_Optimization.quantum-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.python-optimization-dpo-samplehealth-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.python-optimization-dpo-sample