logics
Datasets
All datasets matching “logics”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-Bench