datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Open-Omega-Forge-1M
Open-Omega-Forge-1M
Open-Omega-Forge-1M is a carefully curated and optimized collection derived from multiple high-quality datasets, specifically designed to enhance reasoning capabilities across mathematical, scientific, and coding domains. This dataset represents a focused subset that maintains the quality and diversity of reasoning patterns while providing a more manageable size for training and evaluation. A high-quality, compact reasoning dataset designed for mathematics… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Open-Omega-Forge-1M.BLUR
BLUR: A Benchmark for LLM Unlearning Robust to Forget-Retain Overlap
The BLUR dataset expands on existing unlearning benchmarks by providing harder evaluation tasks, combined forget/retain queries, and relearning datasets of varying degrees of difficulty. Despite the benign nature of the queries considered, we find that the performance of existing methods drops significantly when evaluated on BLUR, with simple approaches performing better on average than more recent methods.… See the full description on the dataset page: https://huggingface.co/datasets/forgelab/BLUR.forge
FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios
🌐 Website | 📑 Paper | 💻 Code | 🤗 Dataset
Quick Start
from datasets import load_dataset
ds = load_dataset("AI4Manufacturing/forge", "task1_three_view", split="train")
print(ds[0].keys())
ds[0]["test_image"] # PIL Image
Configs
Core… See the full description on the dataset page: https://huggingface.co/datasets/AI4Manufacturing/forge.hudson-forge-iqr-v2
HF-IQR V2: Hudson Forge Intelligence and Reasoning Benchmark — Version 2
Dataset Overview
Researcher: Billy Davis
Affiliation: Independent Researcher
Location: Lenoir, North Carolina
Date: May 2026
Version: 2.0
Pre-registration timestamp: 2026-05-08T23:56:24Z
Pre-registration hash: d5c693601d590503154d1689cdd025bba797a9b649efb45fed4b564189871854
What This Dataset Is
HF-IQR V2 is a pre-registered multi-round deliberation benchmark evaluating five frontier… See the full description on the dataset page: https://huggingface.co/datasets/Billyrdavis1985/hudson-forge-iqr-v2.hudson-forge-iqr-benchmark
HF-IQR: Hudson Forge Intelligence and Reasoning Benchmark
Overview
HF-IQR is a novel AI reasoning benchmark that measures
reasoning process quality rather than answer correctness.
Standard benchmarks evaluate whether models get the right answer.
HF-IQR evaluates how models reason, where reasoning breaks down,
and whether reasoning holds under deliberation pressure.
Developed by an independent researcher at Hudson Forge IRMB-C,
Lenoir, North Carolina. Self-funded. No… See the full description on the dataset page: https://huggingface.co/datasets/Billyrdavis1985/hudson-forge-iqr-benchmark.LKF-baseline-challenger-forget-eval
LKF baseline data for the Challenger disaster topic: forget evaluation
This dataset holds the forget evaluation questions of the LKF baseline comparison data used in the
paper "Forget Narrowly, Retain Broadly: Unlearning as an Asymmetric Generalization Problem". It
adapts the Challenger disaster topic of the LKF (Lesser-Known Facts) dataset, introduced in
"Unlearning That Lasts" (Singh et al., 2025), to the format of
our unlearning pipeline.
It is not part of the SUITE… See the full description on the dataset page: https://huggingface.co/datasets/apeleg/LKF-baseline-challenger-forget-eval.mem-behave-forgetting
MemBehave: Forgetting
Can a memory-backed assistant forget one person without damaging what it knows about
everyone else?
Each row is one item: a pair of (user, target person) drawn from that user's
conversation history, a natural-language deletion request, and the questions that say
what should and should not survive it. Items are grouped into triplets -- one user
contributing one target at each entanglement level -- so that a difference between levels
cannot be blamed on one… See the full description on the dataset page: https://huggingface.co/datasets/marzinouri/mem-behave-forgetting.forge-reason-v1
FORGE-REASON v1
Dataset Description
FORGE-REASON is the first open-source dataset of red-teamed mathematical proofs
for fine-tuning LLMs on formal logical reasoning. Each entry contains a triple:
flawed_proof → directive4_critique → corrected_proof
Proofs span four mathematical domains: computational complexity theory, number theory,
cryptography (protocol security), and combinatorics.
Intended Use
Fine-tuning open-source LLMs (Llama, Mistral, Qwen) on… See the full description on the dataset page: https://huggingface.co/datasets/Blainer28/forge-reason-v1.Math-Forge-Hard
Math-Forge-Hard Dataset
Overview
The Math-Forge-Hard dataset is a collection of challenging math problems designed to test and improve problem-solving skills. This dataset includes a variety of word problems that cover different mathematical concepts, making it a valuable resource for students, educators, and researchers.
Dataset Details
Modalities
Text: The dataset primarily contains text data, including math word problems.
Formats… See the full description on the dataset page: https://huggingface.co/datasets/prithivMLmods/Math-Forge-Hard.tofu-pair
Dataset Card for TOFU-Pair 🍢👫
TOFU-Pair is a variant of the original TOFU dataset designed to assess unlearning behavior in large language models when only part of a prompt is harmful. In TOFU-Pair, each prompt consists of paired questions where one refers to an author from the forget set and the other refers to an author from the retain set. This structure enables evaluation of whether a model selectively ignores the harmful part of the prompt while correctly answering the… See the full description on the dataset page: https://huggingface.co/datasets/forgelab/tofu-pair.