datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
sat-vl-sft-training-ready-v1
Dataset Summary
NuTonic/sat-bbox-metadata-sft-v1 is a metadata-first, procedural VLM SFT dataset built from an existing “sat-bbox” style dataset tree (Sentinel‑2 chips + per-tile JSON metadata sidecars, optionally paired Mapbox stills).
The goal is to create high-signal, production-shaped supervision for multimodal chat models:
Captioning for satellite chips
Grounding (bounding boxes in normalized coordinates) for land-cover regions
Class-focused captions and absence checks for… See the full description on the dataset page: https://huggingface.co/datasets/NuTonic/sat-vl-sft-training-ready-v1.sat-vl-sft-postprocessed-merged-v1
Dataset Summary
NuTonic/sat-bbox-metadata-sft-v1 is a metadata-first, procedural VLM SFT dataset built from an existing “sat-bbox” style dataset tree (Sentinel‑2 chips + per-tile JSON metadata sidecars, optionally paired Mapbox stills).
The goal is to create high-signal, production-shaped supervision for multimodal chat models:
Captioning for satellite chips
Grounding (bounding boxes in normalized coordinates) for land-cover regions
Class-focused captions and absence checks for… See the full description on the dataset page: https://huggingface.co/datasets/NuTonic/sat-vl-sft-postprocessed-merged-v1.sat-bbox-metadata-sft-v1
Dataset Summary
NuTonic/sat-bbox-metadata-sft-v1 is a metadata-first, procedural VLM SFT dataset built from an existing “sat-bbox” style dataset tree (Sentinel‑2 chips + per-tile JSON metadata sidecars, optionally paired Mapbox stills).
The goal is to create high-signal, production-shaped supervision for multimodal chat models:
Captioning for satellite chips
Grounding (bounding boxes in normalized coordinates) for land-cover regions
Class-focused captions and absence checks for… See the full description on the dataset page: https://huggingface.co/datasets/NuTonic/sat-bbox-metadata-sft-v1.NuminaMath-LEAN-satp-buffer-dspaug-Temp
NuminaMath-LEAN-satp-buffer-dspaug-Temp
Staging buffer for the DSP+ paper-augmentation sweep (2026-04-29). This
is a -Temp variant — lemma_names / lemma_scores are empty and
theorem_uuid is the join key (matches NuminaMath-LEAN-satp.uuid).
Retrieval population + rename-to-uuid happens at the
promote-to-canonical merge step, mirroring the precedent set by
NuminaMath-LEAN-satp-buffer-planf-v1-Temp.
Why this exists
Schema audit on NuminaMath-LEAN-satp-buffer (40,965… See the full description on the dataset page: https://huggingface.co/datasets/ChristianZ97/NuminaMath-LEAN-satp-buffer-dspaug-Temp.NuminaMath-LEAN-satp-v4.27
NuminaMath-LEAN-satp-v4.27
Lean 4 formal-statement + initial proof goal_state pairs over the
NuminaMath-LEAN problem pool, packaged for Lean 4.27.0. This is the main
training set for SATP (Steering Aesop for Theorem Proving) running in a
Lean 4.27 environment.
Every row's formal_statement elaborates cleanly under the pinned toolchain
below, and every goal_state is the pretty-printed goal produced in that
row's own environment — the same rendering the SATP runtime observes at… See the full description on the dataset page: https://huggingface.co/datasets/ChristianZ97/NuminaMath-LEAN-satp-v4.27.Muse-Glimmer-SWE-Gym-2k
Muse-Glimmer-SWE-Gym-2k
Agentic coding traces from meta-models/Muse-Glimmer-30B, recorded for training a
speculative-decoding drafter. 1,981 mini-swe-agent trajectories over SWE-Gym and
SWE-bench-extra instances, and the 159,999 individual chat-completion calls behind them.
Configs
Config
Rows
Size
What it is
train
1,981
57 MB
One row per trajectory: the full conversation as messages.
raw
159,999
2.7 GB
One row per recorded API call: request and… See the full description on the dataset page: https://huggingface.co/datasets/Satgoy152/Muse-Glimmer-SWE-Gym-2k.math_benchmark_test_saturation
LLM Leaderboard Data for Hendrycks MATH Dataset (2022–2024)
This dataset aggregates yearly performance (2022–2024) of large language models (LLMs) on the Hendrycks MATH benchmark. It is specifically compiled to explore performance evolution, benchmark saturation, parameter scaling trends, and evaluation metrics of foundation models solving complex math word problems.
Original source data: Math Word Problem Solving on MATH (Papers with Code)
About Hendrycks' MATH… See the full description on the dataset page: https://huggingface.co/datasets/nlile/math_benchmark_test_saturation.satSAT (Style Augmented Translation) dataset contains roughly 3.3 million English-Vietnamese pairs of texts.minif2f-satp-alphaproof
minif2f-satp-alphaproof
The canonical 488-base-problem view of the miniF2F benchmark used by
AlphaProof, ported to Lean 4.26.0 and packaged with initial proof
goal_state values generated for SATP v2.
This dataset is intended as the held-out evaluation (test) and
hyperparameter-tuning (validation) benchmark for SATP / sketch-and-prove
pipelines running in the same Lean 4.26 environment.
The source is Google DeepMind
miniF2F@f0a20e1.
Its README identifies this as the benchmark… See the full description on the dataset page: https://huggingface.co/datasets/ChristianZ97/minif2f-satp-alphaproof.NuminaMath-LEAN-satp
NuminaMath-LEAN-satp
Lean 4 formal-statement + initial proof goal_state pairs harvested from
the NuminaMath-LEAN problem pool. This is the main training set for
SATP (Steering Aesop for Theorem Proving) and the target distribution
that all sibling datasets in this collection align with byte-for-byte.
Sibling datasets (same uuid scheme so they join cleanly):
NuminaMath-LEAN-satp-gaps — augmented train set with sub-goal (gap) records harvested from verified sketches… See the full description on the dataset page: https://huggingface.co/datasets/ChristianZ97/NuminaMath-LEAN-satp.Qwen3.5-0.8B-selfdistill
Qwen3.5-0.8B self-distillation pairs (DSpark drafter training data)
The exact training data behind
satgeze/Qwen3.5-0.8B-DSpark: public
prompts answered by the target model itself, so a drafter trains on precisely the distribution
it will draft for at inference. The unique part is the responses; they were generated by
Qwen3.5-0.8B and exist nowhere upstream.
Provenance, exactly
part
source
license
prompts
mlabonne/open-perfectblend
Apache-2.0
responses… See the full description on the dataset page: https://huggingface.co/datasets/satgeze/Qwen3.5-0.8B-selfdistill.indicCorpv2 IndicCORPV2 is the largest collection of texts for Indic langauges consisting of 20.9 Billion tokens of which 14.4B tokens correspond to 23 Indic languages and 6.B tokens of Indian English content curated from Indian websites.ukr-tg-satire
Ukrainian Telegram satire & troll posts
A corpus of 92k posts from 17 public Telegram channels in the
satire/irony/parody register, harvested July 2026 via the public Telegram
API (history reaching back to 2018 for some channels). The channels are
Ukrainian-audience but the posts are a Ukrainian/Russian mix (the
main truha and sria_news feeds write mostly in Russian, the regional
truexa* branches mostly in Ukrainian), so the corpus carries
language: [ru, uk].
Two flavors are… See the full description on the dataset page: https://huggingface.co/datasets/hausmer/ukr-tg-satire.SATQuest
SATQuest Dataset
Paper: SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs
TL;DR. Synthetic CNF benchmark for LLM reasoning: 140 matched SAT/UNSAT pairs with n in [3, 16] and fixed ratio m=4n. The dataset stores only CNF formulas and solver stats; use the SATQuest Python library to render prompts/answers for SATDP, SATSP, MaxSAT, MCS, and MUS in four formats (math, DIMACS, story, dual story).
Data fields
id: unique identifier… See the full description on the dataset page: https://huggingface.co/datasets/sdpkjc/SATQuest.BhojpuriCorpus
Bhojpuri Corpus (BhojpuriCorpus) — Monolingual Pretraining Dataset for Bhojpuri (bho)
Overview
BhojpuriCorpus is a monolingual pretraining dataset for the Bhojpuri language (ISO 639-3: bho), containing 386,032 documents and approximately 24.97 Million estimated tokens. It is compiled from multiple public sources and preprocessed for vocabulary training and language model pretraining.
Motivation
BhojpuriCorpus was compiled to aggregate, clean, and… See the full description on the dataset page: https://huggingface.co/datasets/Satyam810/BhojpuriCorpus.satsec-decomposition
SatSec Grounded Objective-Decomposition Dataset
Version 2.0 is a leakage-controlled replacement for the original dataset used in
A Controlled Candidate-Set Benchmark for Offline Satellite-Security Plan
Decomposition
(DOI 10.48550/arXiv.2607.26371).
It contains 24 authored full decompositions and 83 mechanically derived next-step rows
across 24 cases.
There are 82 train rows and 25 test rows; the six test cases never occur in training.
Important v2 correction
The… See the full description on the dataset page: https://huggingface.co/datasets/paolocmo/satsec-decomposition.ukr-tg-satire-2
TG channels wave 2
A second scrape wave of 6 public Telegram channels
exported 2026-09-15 from their full histories via a
read-only MTProto API. Same schema as the main corpora
(hausmer/ukr-tg-media,
hausmer/ukr-tg-satire).
24,743 text posts across:
Foma_memes — Мемарня Sa-chan1917|ИзюмТГ (1,892 posts)
zelenskyi_vladimir — Владимир Зеленский (пародия) (612 posts)
karikaturnaya_satira — Карикатурная сатира (138 posts)
memarnya_rezerv — Ініціативна група «20 см» (1,169 posts)… See the full description on the dataset page: https://huggingface.co/datasets/hausmer/ukr-tg-satire-2.Chameleon-Radiology-Reportsminif2f-satp-v4.27
minif2f-satp-v4.27
The 488-base-problem view of the miniF2F benchmark used by AlphaProof,
packaged for Lean 4.27.0 with initial proof goal_state values generated
for SATP v2.
This dataset is intended as the held-out evaluation (test) and
hyperparameter-tuning (validation) benchmark for SATP / sketch-and-prove
pipelines running in a Lean 4.27 environment.
Source
The single source of the problem statements is Google DeepMind
miniF2F@f0a20e1
(MiniF2F/Valid.lean +… See the full description on the dataset page: https://huggingface.co/datasets/ChristianZ97/minif2f-satp-v4.27.truha-news-satire
Truha news satire (Ukrainian)
A synthetic, LLM-generated dataset of Ukrainian "news" written in the
truha satirical register — short, meta-ironic, punchline-driven fake-news
items. The corpus was produced by distilling a target style (a Ukrainian
satirical news persona) into generated examples; no real user data is
included.
Intended use: style-transfer / imitation training for a Ukrainian satirical
news-writing assistant. Each example is a (system, instruction, output) triple.… See the full description on the dataset page: https://huggingface.co/datasets/hausmer/truha-news-satire.Qwen3.6-27B-selfdistill
Qwen3.6-27B self-distillation pairs (DSpark drafter training data)
The exact training data behind
satgeze/Qwen3.6-27B-DSpark (2.5-2.7x
measured decode speedup in llama.cpp): public prompts answered by the target model itself, so
the drafter trains on precisely the distribution it drafts for at inference. The responses were
generated by Qwen3.6-27B and exist nowhere upstream.
Provenance, exactly
part
source
license
prompts
mlabonne/open-perfectblend (12K… See the full description on the dataset page: https://huggingface.co/datasets/satgeze/Qwen3.6-27B-selfdistill.putnambench-satp
PutnamBench-SATP — Lean 4 (672 problems, SATP-normalized)
PutnamBench Lean 4 problems normalized for the SATP-DSP-Eval pipeline.
Upstream: trishullab/PutnamBench
(lean4/src/*.lean + informal/putnam.json, parsed via this repo's
datasets/putnam_bench/parse_lean4.py).
Differences from upstream
Field
Upstream
This repo
formal_statement
ends with := sorry
normalized to := by (no trailing tactic)
uuid
absent
sha256(canonical(formal_statement))[:16] after… See the full description on the dataset page: https://huggingface.co/datasets/ChristianZ97/putnambench-satp.prohibition-neglect-corpus
Prohibition Neglect — synthetic corpus
Training corpus for an extension of Negation Neglect (Mayne et al.,
arXiv:2605.13829) to a mechanically-checkable
behaviour: does finetuning on documentation that forbids an API call teach the
call anyway?
4000 synthetic internal-engineering documents about a fictional Python
pipeline library, rendered into 5 arms from one shared set of document
specifications, so the arms differ only in how a prohibition is attached.
⚠️… See the full description on the dataset page: https://huggingface.co/datasets/satchel-goodfire/prohibition-neglect-corpus.dostoyevsky_chunks
Dostoyevsky Chunks Dataset
This dataset contains preprocessed text chunks from four major works by Fyodor Dostoyevsky, prepared for fine-tuning language models on the author's distinctive writing style.
Dataset Description
Dataset Summary
A curated collection of text segments extracted from public domain English translations of Dostoyevsky's novels, processed into consistent chunks suitable for causal language modeling tasks.
Supported Tasks
Causal… See the full description on the dataset page: https://huggingface.co/datasets/satyapratheek/dostoyevsky_chunks.opengenome2
OpenGenome2
OpenGenome2 is a database of nearly 9 trillion base pairs of curated DNA from across all domains of life. Collected from diverse species and public data sources, OpenGenome2 was used to train Evo 2 models. Please refer to the Evo 2 preprint or github repository for further details and usage examples.
We provide OpenGenome2 in two formats, the dataset is organized into two main directories to reflect this:
fasta which contain the DNA sequences
jsonl which include… See the full description on the dataset page: https://huggingface.co/datasets/satputekuldip/opengenome2.storyengine-dataset
StoryEngine Interactive Fiction Dataset
A synthetic dataset of 3,140 interactive fiction conversations designed to fine-tune small language models for guided narrative experiences. Each example follows a structured chat format where the model acts as a storyteller, presenting scenes and meaningful choices to the player.
This dataset was used to train SatorTenet/StoryEngine-2B.
Dataset Description
Overview
The dataset was synthetically generated using… See the full description on the dataset page: https://huggingface.co/datasets/SatorTenet/storyengine-dataset.SurgWound
Dataset Card for SurgWound
SurgWound is the first open-source dataset for surgical wound analysis across multiple procedure types.
SurgWound comprises 697 surgical wound images, each annotated by surgical experts at The Ohio State University Wexner Medical Center (OSWUMC).
Each image is accompanied by high-quality labels covering six surgical wound characteristic attributes and two diagnostic outcomes attributes.
SurgWound-Bench is the first multimodal benchmark for surgical… See the full description on the dataset page: https://huggingface.co/datasets/SATHEESHMA/SurgWound.proofnet-satp-v4.27
ProofNet#-SATP v4.27 — Lean 4 (371 problems)
ProofNet# (corrected reference
formalizations of ProofNet) normalized to the ChristianZ97/putnambench-satp-v4.27
schema for the SATP-DSP-Eval pipeline.
Provenance
Source: PAug/ProofNetSharp
@ a8da405fbd1e348a87445c2e562c747b7e26dc8f (MIT), 371 rows = 185 valid + 186 test.
Introduced in Reliable Evaluation and Benchmarks for Statement Autoformalization
(Poiroux, Weiss, Kunčak, Bosselut; EMNLP 2025 main;… See the full description on the dataset page: https://huggingface.co/datasets/ChristianZ97/proofnet-satp-v4.27.putnambench-satp-v4.27
putnambench-satp-v4.27
PutnamBench Lean 4 problems normalized for SATP evaluation, packaged for
Lean 4.27.0 with initial proof goal_state values.
Source
The single source is trishullab/PutnamBench@dc18909
(lean4/src/putnam_*.lean + informal/putnam.json); the upstream
lean4/lean-toolchain is leanprover/lean4:v4.27.0. All 672 Lean problems
are included and every row elaborates with zero errors under the pinned
toolchain.
Rows
672 (single train config… See the full description on the dataset page: https://huggingface.co/datasets/ChristianZ97/putnambench-satp-v4.27.NuminaMath-LEAN-satp-gaps
NuminaMath-LEAN-satp-gaps
Lean 4 sub-goal (gap) dataset harvested from natural-language draft → Lean sketch → real-Lean goal-state extraction over the NuminaMath-LEAN formal statement pool. Each row is one open hole (sorry) inside a sketch, paired with the exact Lean goal-state at that hole, suitable as a per-sub-goal prove-step training signal.
This is the augmented training set complement to the entry-point
NuminaMath-LEAN-satp
main training set: where the main set carries one… See the full description on the dataset page: https://huggingface.co/datasets/ChristianZ97/NuminaMath-LEAN-satp-gaps.