UI-Mate-democua

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What UI-Mate-democua is

UI-Mate-democua-27B UI-Mate: Advancing Open-Weight Foundation GUI Agents with In-Context Demonstrations Show the workflow once. Let the agent adapt it to the task at hand. Tencent HY Frontier Project Page · GitHub · arXiv Overview UI-Mate-democua-27B is the demonstration-guided checkpoint of UI-Mate. It observes live screenshots, reasons over the visible state, and produces structured keyboard and mouse actions, and it can additionally take one recorded workflow as input and carry that procedure over to a new task. It starts from the general reinforcement-learning checkpoint and is further trained with supervised fine-tuning on a mixture of general computer-use data and demonstration-augmented data. Keeping general data in the mixture is what preserves instruction-only competence: the model still runs a task from an instruction alone, and gains the ability to use a demonstration when one is supplied. A demonstration is treated as guidance rather than a fixed action script. Recorded coordinates are never replayed, and the model re-plans from the live interface whenever the content, layout, or application state differs. Model Details - Parameters: 27B - Base model: Qwen3.6-27B - Input: task instruction, screenshots, interaction history, and an optional demonstration workflow - Output: reasoning, a concise action description, and structured computer-use tool calls - Action space: mouse, keyboard, scrolling, waiting, user interaction, subtask completion, and task completion - Training: supervised fine-tuning, online reinforcement learning in executable GUI environments, then supervised fine-tuning on mixed general and demonstration-augmented computer-use data - License: Apache-2.0 UI-Mate is an agent checkpoint rather than a standalone visual-chat model. We recommend using the official prompt, response parser, and interaction harness from the UI-Mate repository. Highlights - One-shot procedural learning from a single demonstration. - Instruction-only execution retained through mixed general and demonstration training data. - Subtask-level guidance rather than whole-workflow injection. - Live-screen grounding instead of coordinate replay. - Trained on incomplete work

Summarised from the published model card. Read the full card on the HuggingFace links below.

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Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys ui-mate-democua for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (ui-mate-democua below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"ui-mate-democua","messages":[{"role":"user","content":"Hello"}]}'

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