datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ESL-Bench
ESL-bench
ESL-bench (Event-driven Synthetic Longitudinal Benchmark) is a virtual health user dataset for evaluating AI health assistants. Each virtual user contains a complete health profile, event timeline, clinical exam data, and knowledge-graph-grounded evaluation queries, designed for use with the Mirobody-Eval framework.
⚠️ Research use only. Outputs are synthetic and intended for benchmarking AI agents. They should not be used for diagnosis or treatment decisions.… See the full description on the dataset page: https://huggingface.co/datasets/mirobody/ESL-Bench.MedHall-Bench
MedHall-Bench
MedHall-Bench is a field-grounded hallucination detection benchmark for medical AI assistants. It decomposes each clinical response into verifiable structured fields (dose value, unit, reference range, ICD/LOINC code, entity relation, ...) and evaluates AI outputs via per-field programmatic matching in addition to sentence-level LLM-as-Judge. Designed for use with the HolyEval framework.
⚠️ Research use only. Content is for benchmarking AI agents and should not be… See the full description on the dataset page: https://huggingface.co/datasets/mirobody/MedHall-Bench.MedHarm-Bench
MedHarm-Bench
MedHarm-Bench is a red-team compliance benchmark for health-management AI assistants. It uses natural-sounding patient questions that bait the assistant into crossing medical safety boundaries, then scores each response against compliance red lines. Designed for use with the HolyEval framework.
⚠️ Research use only. Questions are designed to elicit unsafe behavior for benchmarking purposes and should not be used for diagnosis or treatment decisions.… See the full description on the dataset page: https://huggingface.co/datasets/mirobody/MedHarm-Bench.MiroVerse-v0.1MiroMind-M1-SFT-719K
MiroMind-M1
🧾 Overview
Training performance of MiroMind-M1-RL-7B on AIME24 and AIME25.
MiroMind-M1 is a fully open-source series of reasoning language models built on Qwen-2.5, focused on advancing mathematical reasoning. It is trained through supervised fine-tuning (SFT) on 719K curated problems and reinforcement learning with verifiable rewards (RLVR) on 62K challenging examples, using a context-aware multi-stage policy optimization method… See the full description on the dataset page: https://huggingface.co/datasets/miromind-ai/MiroMind-M1-SFT-719K.MiroVerse-v0.1
MiroVerse: A Reproducible, Full-Trajectory, Ever-Growing Deep Research Dataset
🔥 News & Updates
MiroVerse v0.1 has been released. This dataset can be used with our training framework, MiroTrain. In MiroVerse v0.1, we provide both SFT and DPO data, making it easy to reproduce MiroThinker-v0.1’s benchmark performance on Qwen3. Give it a try!
The initial release of MiroVerse (v0.1) is coming this Friday—stay tuned!
🔥 First Batch of MiroVerse… See the full description on the dataset page: https://huggingface.co/datasets/miromind-ai/MiroVerse-v0.1.MiroMind-M1-RL-62K
MiroMind-M1
🧾 Overview
Training performance of MiroMind-M1-RL-7B on AIME24 and AIME25.
MiroMind-M1 is a fully open-source series of reasoning language models built on Qwen-2.5, focused on advancing mathematical reasoning. It is trained through supervised fine-tuning (SFT) on 719K curated problems and reinforcement learning with verifiable rewards (RLVR) on 62K challenging examples, using a context-aware multi-stage policy optimization method… See the full description on the dataset page: https://huggingface.co/datasets/miromind-ai/MiroMind-M1-RL-62K.MiroMind-SFTLingxiDiag-16K
LingxiDiag-16K
A Large-Scale Synthetic Psychiatric Dialogue Dataset for Diagnostic Decision Support
Overview
LingxiDiag-16K is a synthetic psychiatric dialogue dataset containing approximately 16,000 electronic medical records (EMRs) and doctor-patient consultation dialogues.
The dataset is designed for evaluating and training LLM-based psychiatric diagnostic decision support systems, with demographically aligned distributions reflecting real-world clinical… See the full description on the dataset page: https://huggingface.co/datasets/mirobody/LingxiDiag-16K.MiroMind-M1-SFT-719K-transformedMiroRL-GenQA
MiroRL-GenQA
A curated dataset for reinforcement learning (RL) training within the MiroRL framework.
Overview
Source: Provided by MiroMind AI as part of the MiroRL project.
Format & Size: Contains ~13.1k examples in Parquet format for efficient loading and processing.
License: Released under CC-BY-NC-4.0 for non-commercial use.
Purpose: Designed to serve as high-quality input for RL fine-tuning in the MiroRL pipeline.
Dataset Structure
Each record… See the full description on the dataset page: https://huggingface.co/datasets/miromind-ai/MiroRL-GenQA.agent-reliability-corpussocial-prediction-market-sim
MiroShark Social + Prediction Market Simulation
Agent decisions from MiroShark simulations (GitHub). In each simulation, LLM agents with distinct personas (companies, founders, communities, regulators, commentators) share a Twitter/Reddit-style feed and a Polymarket-style prediction market. Every round, each agent reads the feed (or its portfolio and the open markets) and decides what to do: post, comment, quote, like, follow, buy or sell shares, or do nothing.
Each row is one… See the full description on the dataset page: https://huggingface.co/datasets/MiroShark/social-prediction-market-sim.Science_ArticlesMIRON_Benchmark
M.I.R.O.N. (Multi-aspect Inference Robustness on Objective Next-tokens)
M.I.R.O.N. is a specialized benchmark designed to evaluate the impact of tokenization and architectural constraints on the generation quality of small, Base language models (SLMs).
Unlike global benchmarks (MMLU, GSM8K), MIRON focuses on the atomic capabilities of a model: morphological generalization, noise robustness, and factual integrity within a simple next-token prediction task.
🎯 Main Goal… See the full description on the dataset page: https://huggingface.co/datasets/apsua/MIRON_Benchmark.2025-automobile-ai-object-detectionopenseeker-miroverse-mix-full
OpenSeeker + MiroVerse SFT mix (full)
All-data union of two deep-research agent SFT datasets in the OpenDR-eval agent wire
format (OpenAI-native messages, tools = search/visit, final answer wrapped in
<answer>...</answer>).
split
rows
composition
train
28691
4885 OpenSeeker + 23806 MiroVerse
validation
320
held-out
Columns: messages, tools, question, answer, n_tool_calls, source.
Why "full" rather than 1:1-by-rows
The earlier… See the full description on the dataset page: https://huggingface.co/datasets/Zephyr271828/openseeker-miroverse-mix-full.verified_miro_trajectoriesopenseeker-miroverse-mix-1to1
OpenSeeker + MiroVerse 1:1 SFT mix
A 1:1 (by row count) mix of two deep-research agent SFT datasets, in the OpenDR-eval agent
wire format (OpenAI-native messages, tools = search/visit, final answer in <answer>...</answer>).
split
rows
composition
train
9,770
4,885 OpenSeeker + 4,885 MiroVerse
validation
128
64 + 64
Columns: messages, tools, question, answer, n_tool_calls, source.
OpenSeeker half: from Zephyr271828/openseeker_v1_sft.
MiroVerse half: HotpotQA /… See the full description on the dataset page: https://huggingface.co/datasets/Zephyr271828/openseeker-miroverse-mix-1to1.formatted_miromind-1000uladzimir-mironchyk-gistarychnaia-paema-uladzimir-mironchyk
Гістарычная паэма
Metadata
Author: Уладзімір Мірончык
Title: Гістарычная паэма
Narrator: Уладзімір Мірончык
Source Group: Аўдыёкнігі
Source:
Notes
The original audio files are preserved as-is:
no conversion;
no re-encoding;
no filename changes inside each split folder, except removing one common top-level archive folder when present.
To avoid Hugging Face Dataset Viewer scan-size errors, the dataset is split into smaller folders.
Target maximum… See the full description on the dataset page: https://huggingface.co/datasets/archivartaunik/uladzimir-mironchyk-gistarychnaia-paema-uladzimir-mironchyk.NLP_1
Dataset Card for "Text"
More Information needed
MiroMind-SFT-miniuladzimir-mironchyk-skarbonka-uladzimir-mironchyk
Скарбонка
Metadata
Author: Уладзімір Мірончык
Title: Скарбонка
Narrator: Уладзімір Мірончык
Source Group: Аўдыёкнігі
Source: https://www.youtube.com/channel/UCfx2VYH2i7VZXb7txMBBiyQ
Notes
The original audio files are preserved as-is:
no conversion;
no re-encoding;
no filename changes inside each split folder, except removing one common top-level archive folder when present.
To avoid Hugging Face Dataset Viewer scan-size errors, the dataset is… See the full description on the dataset page: https://huggingface.co/datasets/archivartaunik/uladzimir-mironchyk-skarbonka-uladzimir-mironchyk.Miroir-IME
Miroir-IME — Dataset d'écriture manuscrite (ODbL)
Dataset de paires (geste, label) capturées sur Boox Note Air 5C (stylet e-ink)
via le Miroir IME. Format parnasse-dataset.v1.
📜 Licence
ODbL (Open Database License) — attribution requise, partage à l'identique.
📊 Contenu
Fichier
Échantillons
Points
parnasse-dataset-full-*.jsonl
~400
~420 000
Chaque ligne = un échantillon JSON :
{
"sample_id": "sha256...",
"label": "philosophie"… See the full description on the dataset page: https://huggingface.co/datasets/nctahiti/Miroir-IME.HollowPUBustaunaia-gramata-bnr-uladzimir-mironchyk
Устаўная грамата БНР
Metadata
Author:
Title: Устаўная грамата БНР
Narrator: Уладзімір Мірончык
Source Group: Аўдыёкнігі
Source:
Notes
The original audio files are preserved as-is:
no conversion;
no re-encoding;
no filename changes inside each split folder, except removing one common top-level archive folder when present.
To avoid Hugging Face Dataset Viewer scan-size errors, the dataset is split into smaller folders.
Target maximum split size:… See the full description on the dataset page: https://huggingface.co/datasets/archivartaunik/ustaunaia-gramata-bnr-uladzimir-mironchyk.uladzimir-mironchyk-marshrutka-uladzimir-mironchyk
Маршрутка
Metadata
Author: Уладзімір Мірончык
Title: Маршрутка
Narrator: Уладзімір Мірончык
Source Group: Аўдыёкнігі
Source: https://www.youtube.com/channel/UCfx2VYH2i7VZXb7txMBBiyQ
Notes
The original audio files are preserved as-is:
no conversion;
no re-encoding;
no filename changes inside each split folder, except removing one common top-level archive folder when present.
To avoid Hugging Face Dataset Viewer scan-size errors, the dataset is… See the full description on the dataset page: https://huggingface.co/datasets/archivartaunik/uladzimir-mironchyk-marshrutka-uladzimir-mironchyk.uladzimir-mironchyk-smely-rytsar-uladzimir-mironchyk
Смелы рыцар
Metadata
Author: Уладзімір Мірончык
Title: Смелы рыцар
Narrator: Уладзімір Мірончык
Source Group: Аўдыёкнігі
Source:
Notes
The original audio files are preserved as-is:
no conversion;
no re-encoding;
no filename changes inside each split folder, except removing one common top-level archive folder when present.
To avoid Hugging Face Dataset Viewer scan-size errors, the dataset is split into smaller folders.
Target maximum split size:… See the full description on the dataset page: https://huggingface.co/datasets/archivartaunik/uladzimir-mironchyk-smely-rytsar-uladzimir-mironchyk.combined-sft-training-data-v20250824_MiroSystemPrompt