datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
NuminaMath-CoT
Dataset Card for NuminaMath CoT
Dataset Summary
Approximately 860k math problems, where each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese high school math exercises to US and international mathematics olympiad competition problems. The data were primarily collected from online exam paper PDFs and mathematics discussion forums. The processing steps include (a) OCR from the original PDFs, (b) segmentation… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/NuminaMath-CoT.olympiads
AI-MO Olympiad Reference Dataset
This dataset contains a structured collection of Olympiad problems and their solutions,
organized by competition. Contains high quality data, prioritizing "official" solutions to problems.
Structure
<competition name>/ # Problems and solutions from the International Mathematical Olympiad
├── raw/ # Raw problem/solution statements (.pdf)
│ ├── file1.pdf
│ ├── file2.pdf
├── download_script/ # the scripts used to… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/olympiads.NuminaMath-1.5
Dataset Card for NuminaMath 1.5
Dataset Summary
This is the second iteration of the popular NuminaMath dataset, bringing high quality post-training data for approximately 900k competition-level math problems. Each solution is formatted in a Chain of Thought (CoT) manner. The sources of the dataset range from Chinese high school math exercises to US and international mathematics olympiad competition problems. The data were primarily collected from online exam paper PDFs… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/NuminaMath-1.5.aimo-validation-aime
Dataset Card for AIMO Validation AIME
All 90 problems come from AIME 22, AIME 23, and AIME 24, and have been extracted directly from the AOPS wiki page https://artofproblemsolving.com/wiki/index.php/AIME_Problems_and_Solutions
This dataset serves as an internal validation set during our participation in the AIMO progress prize competition. Using data after 2021 is to avoid potential overlap with the MATH training set.
Here are the different columns in the dataset:
problem: the… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/aimo-validation-aime.aops
AoPS: Art of Problem Solving Competition Mathematics
Dataset Description
This dataset is a collection of 80,661 competition mathematics problems and solutions obtained from the Art of Problem Solving (AoPS) community wiki and forums. It covers a wide range of mathematical contests and olympiads, including problems from events such as AIME, BAMO, IMO, and various national and memorial competitions.
The dataset was curated by AI-MO (Project Numina), an initiative focused on… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/aops.ai-model-popularity
Datamata AI Model Popularity Index
Weekly popularity of the most-downloaded and trending Hugging Face models: trailing downloads, likes, the model's task and its trending rank. One row per model from the most recent weekly snapshot.
Latest snapshot: 2026-09-20
Models in this release: 50
Updated: weekly
Licence: CC BY 4.0 — free to use and adapt, including commercially, with attribution.
Source & methodology: https://www.datamatastudios.com/datasets
Quickstart… See the full description on the dataset page: https://huggingface.co/datasets/datamatastudios/ai-model-popularity.aimo-validation-amc
Dataset Card for AIMO Validation AMC
All 83 come from AMC12 2022, AMC12 2023, and have been extracted from the AOPS wiki page https://artofproblemsolving.com/wiki/index.php/AMC_12_Problems_and_Solutions
This dataset serves as an internal validation set during our participation in the AIMO progress prize competition. Using data after 2021 is to avoid potential overlap with the MATH training set.
Here are the different columns in the dataset:
problem: the modified problem statement… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/aimo-validation-amc.NuminaMath-TIR
Dataset Card for NuminaMath CoT
Dataset Summary
Tool-integrated reasoning (TIR) plays a crucial role in this competition. However, collecting and annotating such data is both costly and time-consuming. To address this, we selected approximately 70k problems from the NuminaMath-CoT dataset, focusing on those with numerical outputs, most of which are integers. We then utilized a pipeline leveraging GPT-4 to generate TORA-like reasoning paths, executing the code and… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/NuminaMath-TIR.aimotive-multimodal
Dataset Card for aiMotive Multimodal Dataset
The aiMotive Multimodal Dataset is a 176-scene autonomous driving dataset
with synchronized and calibrated LiDAR, camera, and radar sensors providing
360-degree field-of-view coverage with sensor redundancy. Scenes were
captured in highway, urban, and suburban environments across three countries
during daytime, night, and rain. The dataset contains 26,583 annotated
frames with 3D bounding boxes for 14 object classes (425k+ instances)… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/aimotive-multimodal.aimo-validation-math-level-5
Dataset Card for AIMO Validation MATH Level 5
A subset of level 5 problems from https://huggingface.co/datasets/lighteval/MATH
We have extracted the final answer from boxed, and only keep those with integer outputs.
NuminaMath-LEAN
Dataset Card for NuminaMath-LEAN
Dataset Summary
NuminaMath-LEAN is a large-scale dataset of 100K mathematical competition problems formalized in Lean 4. It is derived from a challenging subset of the NuminaMath 1.5 dataset, focusing on problems from prestigious competitions like the IMO and USAMO. It represents the largest collection of human-annotated formal statements and proofs designed for training and evaluating automated theorem provers. This is also the dataset… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/NuminaMath-LEAN.aimo-validation-math-level-4
Dataset Card for AIMO Validation MATH Level 4
A subset of level 4 problems from https://huggingface.co/datasets/lighteval/MATH
We have extracted the final answer from boxed, and only keep those with integer outputs.
amc_filteredaimodelpaintingaime_filteredgpt-oss-120b-high-reasoning-firsthalf-g2-statsPrivate artifact repo for GPT-OSS-120B G^2 collection.
Selection:
dataset: aimosprite/high-reasoning-eval
usage half: partitions 0,1,2
selected traces: 183
Contents:
g2_stats/: per-layer sum_g2 safetensors for routed MoE weights
selected_traces_manifest.json: trace selection manifest
selected_traces.jsonl: exact selected traces used for stats collection
Run family:
shared base strategy: zero
target rank for downstream materialization: 256
model: unsloth/gpt-oss-120b-BF16
prompt-swap-mixed12-5xlr-e1-mxfp4-mergedprompt-swap-mixed12-5xlr-e2-mxfp4-mergedrlhflow_mix_w_aimo_mathCombiBench
CombiBench
CombiBench is the first benchmark focused on combinatorial problems, based on the formal language Lean 4. CombiBench is a manually produced benchmark, including 100 combinatorial mathematics problems of varying difficulty and knowledge levels. It aims to provide a benchmark for evaluating the combinatorial mathematics capabilities of automated theorem proving systems to advance the field. For problems that require providing a solution first and then… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/CombiBench.olympiads-ref-baseaimodelsAll of my models posted on AI HUB
ai-model-pricing-daily
AI Model Pricing Daily
A daily snapshot of AI model pricing and metadata — flagship and open models across
providers (OpenAI, Anthropic, Google, Mistral, Groq, ...) — exported through
Dynamic Feed, a live, verifiable data API whose every response
is Ed25519-signed. One file per day (data/YYYY-MM-DD.jsonl), one JSON object per
model per line. Because model prices change without notice and post-date every model's
training cutoff, a dated, signed daily series is the form this data… See the full description on the dataset page: https://huggingface.co/datasets/dynamicfeed/ai-model-pricing-daily.aimo_progress_prize_1PolyUniMath
PolyUniMath
Dataset Summary
PolyUniMath is a large multilingual mathematics dataset of question-solution-answer pairs extracted from mathematical PDFs.
The dataset is designed for training and studying natural-language mathematical reasoning, with a strong emphasis on university-level content.
Sample count: approximately 4 million Q&A pairs
Main focus: university-level mathematics
Format: problem, optional choices, solution, final answer, and auxiliary… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/PolyUniMath.Kimina-Prover-Promptset
Kimina-Prover-Promptset
Kimina-Prover-Promptset is a curated subset of NuminaMath-LEAN, designed for reinforcement learning (RL) training of formal theorem provers in Lean 4.
Compared to the full dataset, this subset contains fewer problems but with higher difficulty.
NuminaMath-LEAN is filtered and preprocessed as follows to create this dataset:
Remove easy problems with a historical win rate above 0.5 to only keeep challenging statements in the dataset.
Generate variants of… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/Kimina-Prover-Promptset.prompt-swap-medium12-e2-mxfp4-mergedval-sampleminif2f_test
MiniF2F
Dataset Usage
The evaluation results of Kimina-Prover presented in our work are all based on this MiniF2F test set.
Improvements
We corrected several erroneous formalizations, since the original formal statements could not be proven. We list them in the following table. All our improvements are made based on the MiniF2F test set provided by DeepseekProverV1.5, which applies certain modifications to the original dataset to adapt it to the Lean 4.… See the full description on the dataset page: https://huggingface.co/datasets/AI-MO/minif2f_test.PolyMath
Dataset Card for PolyMath
Dataset Summary
PolyMath is a curated dataset of 11,090 high-difficulty mathematical problems designed for training reasoning models. Built for the AIMO Math Corpus Prize. Existing math datasets (NuminaMath-1.5, OpenMathReasoning) suffer from high noise rates in their hardest samples and largely unusable proof-based problems.
PolyMath addresses both issues through:
Data scraping: problems sourced from official competition PDFs absent from… See the full description on the dataset page: https://huggingface.co/datasets/AIMO-Corpus/PolyMath.