datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SWE-Star
SWE-Star
Introduction
SWE-Star is a family of language models based on the Qwen2.5-Coder family and trained on the SWE-Star dataset. The dataset contains approximately 250k agentic coding trajectories distilled from Devstral-2-Small using SWE-Smith tasks.
The complete data generation, training, and evaluation pipeline is openly available in our GitHub repository, enabling anyone to reproduce our results.
Additional details are available in our blog posts.… See the full description on the dataset page: https://huggingface.co/datasets/LogicStar/SWE-Star.Logics-STEM-SFT-Dataset-Open-1.6M
Logics-STEM-SFT-Dataset-2.2M
📰 News
[2026.01.05]🔥 Release of our Techinical Report.
[2026.01.05]🔥 Release the first version of Logics-STEM-8B-SFT, Logics-STEM-8B-RL, /Logics-STEM-SFT-Dataset-Open-1.6M.
Overview
What is this dataset?
Logics-STEM-SFT-Dataset-2.2M is a curated long Chain-of-Thought (CoT) SFT dataset for STEM reasoning, built on top of high-quality open-source data and enhanced through a rigorous curation and distillation… See the full description on the dataset page: https://huggingface.co/datasets/Logics-MLLM/Logics-STEM-SFT-Dataset-Open-1.6M.SWE-Smith
A extended version of the original SWE-smith-py dataset with more problem descriptions!
Logics-STEM-SFT-Dataset-Open-5.3MOmniParsingBench
🤗 Model | 📑 Technical Report | 💻 GitHub
OmniParsingBench is a comprehensive, large-scale, and high-quality evaluation corpus designed to rigorously evaluate the unified parsing capabilities of Multimodal Large Language Models (MLLMs) across diverse modalities.
Unlike traditional single-task benchmarks, OmniParsingBench assesses the full spectrum of parsing performance—from fundamental signal detection to complex semantic reasoning—across six primary domains: Document… See the full description on the dataset page: https://huggingface.co/datasets/Logics-MLLM/OmniParsingBench.SWEE-BenchBaxBench
Dataset Summary
BaxBench is a coding benchmark constructed to measure the ability of code generation models and agents to generate correct and secure code. It consists of 392 backend development tasks, which are constructed by combining 28 scenarios that describe the backend functionalities to implement and 14 backend frameworks defining the implementation tools. To assess the correctness and security of the solutions, the benchmark uses end-to-end functional tests and practical… See the full description on the dataset page: https://huggingface.co/datasets/LogicStar/BaxBench.SWA-Benchdesigner-design-logics
DESIGNER: Design Logic Library [Project Page]
This repository contains a library of Mermaid-format Design Logics used in the paper DESIGNER: Design-Logic-Guided Multidisciplinary Data Synthesis for LLM Reasoning (ICLR 2026).
Field definitions
mermaid: Design Logic in Mermaid format, abstracted from the source question, which is a human-authored high-difficulty question.
difficulty: difficulty label of the source question
type: type label of the source question… See the full description on the dataset page: https://huggingface.co/datasets/Attention1115/designer-design-logics.nikhitha_telugu_datasetLogicStack-LeetCodeextract from LogicStack-LeetCode
公众号「宫水三叶的刷题日记」刷穿 LeetCode 系列文章源码
包括 编程题目、解析、tag、题目url
根据 leetcode 原始题目网页，修正了一些 文件名 和 文件内容 中标注的难度不一致的文件样本
LogicSkills
LogicSkills — Training Data
Companion training releases for the LogicSkills
benchmark (EMNLP 2026 Findings). This Hugging Face dataset has three subsets:
symbolization, countermodel, and validity, each with a single train split.
Each subdirectory contains the corresponding data and task documentation.
Code for our data generation pipeline can be found here.
Dataset
Path
Size
Task
Symbolization
symbolization/
100k × 2 languages
sentence → FOL
Countermodel… See the full description on the dataset page: https://huggingface.co/datasets/mainlp/LogicSkills.armada-logics-ft
Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Dataset Details
Dataset Description
Curated by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Dataset Sources [optional]
Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/ecaccam/armada-logics-ft.Logics-SWE-Env-2.5K
Logics-SWE-Env-2.5K
2,553 software engineering task instances · 1,771 repositories · 4 programming languages
🤗 Related model: Logics-SWE-Qwen3.6-27B
📄 Paper: One to More, More to One
Overview
What is this dataset?
Logics-SWE-Env-2.5K is a collection of repository-level software engineering tasks for research on coding agents and environment-based reinforcement learning. It contains 2,553 unique task instances from 1,771 GitHub repositories… See the full description on the dataset page: https://huggingface.co/datasets/Logics-MLLM/Logics-SWE-Env-2.5K.