gui-agent
smolagents_SmolVLM2-2.2B-Instruct-Agentic-GUI-GGUFAgentPublic-llama3-instruct-guillaumetell-GGUFgemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUFSmolVLM2-2.2B-Instruct-Agentic-GUI-GGUFAgentPublic_-_guillaumetell-7b-ggufAgentCPM-GUISmolVLM2-2.2B-Instruct-Agentic-GUISmolVLM2-2.2B-Agentic-GUI-GGUF
Gui-agent
Gui-Agent — GUI trajectories in LIBERO/VLA format
Human GUI demonstrations from four sources, unified into a single VLA-style
intermediate representation and written as LIBERO-layout HDF5, so LIBERO/VLA
dataloaders run against GUI data unchanged.
raw source ──[adapter]──> GuiEpisode ──[writer]──> LIBERO-style HDF5
per-source the IR format- what you train on
only specific
25,872 episodes / 453,264 steps / 235 GB… See the full description on the dataset page: https://huggingface.co/datasets/Yushi123/Gui-agent.gui-agent-outputDATA_SOURCEagentnet-partial-and-fail-v1
agentnet-partial-and-fail-v1
GUI state transitions (s, a, s') walked on an Ubuntu desktop by
Qwen3.8-27B, from tasks taken from AgentNet and run inside OSWorld's
Docker environment.
Walks the judge ruled partial or failed. These are the larger half and, for a world model, the more useful one: a walk that did not finish still opened dialogs, switched tabs and changed settings, and each of those is a real transition. Measured over eighty-eight walks, a failure visits 13.5 distinct… See the full description on the dataset page: https://huggingface.co/datasets/gui-wm/agentnet-partial-and-fail-v1.agentnet-success-v1
agentnet-success-v1
GUI state transitions (s, a, s') walked on an Ubuntu desktop by
Qwen3.8-27B, from tasks taken from AgentNet and run inside OSWorld's
Docker environment.
Walks the judge ruled had finished the task. Use these to score an agent: the trajectory is a worked example, and action_target gives the element each step was aiming at, so an answer can be marked right by control rather than by pixel.
This repository is the one-condition pool. Tasks are drawn from the 623… See the full description on the dataset page: https://huggingface.co/datasets/gui-wm/agentnet-success-v1.Magic-RICH
Dataset Summary
Magic-RICH is a Chinese benchmark dataset for evaluating mobile GUI agents in realistic smartphone environments. It contains 4,000 step-level samples across four subsets, covering 17 categories and over 150 popular apps. Unlike many previous GUI datasets, Magic-RICH also includes special actions such as screenshot and long screenshot to better reflect real-world interactions. This dataset is designed for evaluation only (no train/dev split) and was used in the… See the full description on the dataset page: https://huggingface.co/datasets/GUIAgent/Magic-RICH.