Synthesizes high-quality targets for real-world image restoration by using multimodal foundation models (MFMs) to convert real low-quality photos into HQ references. Provides GGT-100K (103,707 LQ–HQ training pairs + 500 test pairs) with multi-stage quality control and demonstrates consistent generalization gains for a range of restoration models, especially for finetuning generative restorers.
Provides ~3.8M chain-of-thought training examples (~3B tokens) that use a fixed 'Inventory / Interaction / Execution' scaffold to improve concise, reliable reasoning in small LLMs; topic-labeled, deduped, includes real tool-execution traces, CC-BY-4.0.
Evaluates metric 3D spatial reasoning from single driving images via multiple-choice questions that require reconstructing scene geometry rather than relying on image-layout shortcuts. Each sample pairs a numbered-bbox image with a question, four choices, and the correct answer; images come from PlusAI and the dataset is CC BY 4.0.
A 16 GB, 507-file PhD‑level cybersecurity knowledge base for training and evaluating security-focused LLMs and automation. Covers offensive/defensive/forensics/cloud/iot and AI-security across 30+ domains with real-world labs and framework mappings.
Provides 462 unrestricted long-form chain-of-thought reasoning traces distilled from the full Mythos V2 model (≈104.7M characters); intended for long-context evaluation, trace analysis and process-level supervision. License unknown—verify before reuse.
Provides per-cell transcriptomes and five-day drug-sensitivity readouts for 1.83M single cells across 52 cancer cell lines and 91 drug conditions, with raw counts plus gene, cell-line, drug, and summary metadata for modeling drug response and context-dependent gene function.
Provides the gated, official OSWorld 2.0 Python task class files (task_*.py) required to run the benchmark; distributed via a Hugging Face gated dataset to reduce benchmark leakage. Download requires accepting gated access on Hugging Face.
A benchmark for evaluating web-browsing agents in Korean contexts, composed of 400 tasks (300 manually verified by native speakers). Includes a human-verified split and an adversarial synthetic split to probe failure modes; reveals large performance gaps for both frontier and Korean models.
Benchmark dataset for evaluating long-horizon coding agents and software-engineering tasks, containing English code and tabular metadata in Parquet format; small scale (<1K examples) for fast prototyping and evaluation.
Provides ~1M synthetic Salvadoran‑Spanish personas (148k records, ~300M tokens) grounded in 2024 census distributions for demographics, occupations and locations; intended for training/evaluating localized LLMs and synthetic-data workflows. CC BY 4.0, adults only.
Generates synthetic coding-agent session traces by pairing remotely hosted open agent models with local llama.cpp user models across real open-source codebases. Each trace records read/write/edit/bash actions and tool use; the dataset is a reproducible cartesian product (20×3×20×20 = 24,000 sessions) under an MIT license.
Around 80K short audio clips paired with transcripts in JSON, organized for easy loading with the Hugging Face datasets ecosystem—designed for short-form speech tasks (ASR, TTS, fine-tuning) and quick prototyping with common Python data tools.