--- title: GUI-Owl-7B-bf16 canonical_url: "https://www.modelscope.cn/models/mlx-community/GUI-Owl-7B-bf16" md_url: "https://www.modelscope.cn/models/mlx-community/GUI-Owl-7B-bf16.md" repository: mlx-community/GUI-Owl-7B-bf16 last_updated: 2026-03-20 license: mit pipeline_tag: image-text-to-text tasks: - image-text-to-text model_type: - qwen2_5_vl architectures: - Qwen2_5_VLForConditionalGeneration base_model: - mPLUG/GUI-Owl-7B base_model_relation: finetune parameters: 8.3B tensor_type: - BF16 library_name: - mlx - safetensors - pytorch frameworks: - pytorch language: - en inference_backends: - "deploy_task vlm/text/emb" - "lmdeploy 0.9.1" - "sglang 0.5.2" - "vllm 0.9.2" downloads: 48 stars: 0 tags: - mlx - mlx-vlm - safetensors - apple-silicon - conversational - gui - vision-language-model - qwen2_5_vl - gui-owl - mobile-agent-v3 - computer-use - grounding - osworld - screenspot - bf16 --- # GUI-Owl-7B-bf16 > GUI-Owl-7B-bf16 - mlx-community 在 ModelScope 开源的模型。This is an MLX conversion of mPLUG/GUI-Owl-7B, optimized for Apple Silicon. mlx-community/GUI-Owl-7B-bf16 是 ModelScope 魔搭社区上的 8.3B 参数image-text-to-text模型，采用 mit 许可，基于 mPLUG/GUI-Owl-7B 构建，可用 deploy_task vlm/text/emb、lmdeploy 0.9.1、sglang 0.5.2 部署。 - **Repository**: mlx-community/GUI-Owl-7B-bf16 - **License**: mit - **Tasks**: image-text-to-text - **Parameters**: 8.3B - **Base model**: mPLUG/GUI-Owl-7B - **Inference backends**: deploy_task vlm/text/emb, lmdeploy 0.9.1, sglang 0.5.2, vllm 0.9.2 - **Tags**: mlx, mlx-vlm, safetensors, apple-silicon, conversational, gui, vision-language-model, qwen2_5_vl, gui-owl, mobile-agent-v3, computer-use, grounding, osworld, screenspot, bf16 - **Downloads**: 48 - **Stars**: 0 - **Last updated**: 2026-03-20 Source: https://www.modelscope.cn/models/mlx-community/GUI-Owl-7B-bf16 --- # GUI-Owl-7B bf16 This is an MLX conversion of [mPLUG/GUI-Owl-7B](https://huggingface.co/mPLUG/GUI-Owl-7B), optimized for Apple Silicon. GUI-Owl is a GUI automation model family developed as part of the Mobile-Agent-V3 project. Upstream, it is positioned for screen understanding, GUI grounding, and agentic action planning across benchmark suites such as ScreenSpot and OSWorld-style tasks. This MLX artifact was converted with `mlx-vlm` and validated locally with both `mlx_vlm` prompt-packet checks and `vllm-mlx` OpenAI-compatible serve checks. ## Conversion Details | Field | Value | |---|---| | Upstream model | `mPLUG/GUI-Owl-7B` | | Artifact type | `bf16 MLX conversion` | | Source posture | `direct upstream conversion` | | Conversion tool | `mlx_vlm.convert` via `mlx-vlm 0.3.12` | | Python | `3.11.14` | | MLX | `0.31.0` | | Transformers | `5.2.0` | | Validation backend | `vllm-mlx (phase/p1 @ 8a5d41b)` | | Quantization | `bf16` | | Group size | `n/a` | | Quantization mode | `n/a` | | Artifact size | `15.47G` | | Template repair | `tokenizer_config.json["chat_template"]` was re-injected after conversion | Additional notes: - Direct upstream conversion from `mPLUG/GUI-Owl-7B` succeeded on `mlx-vlm 0.3.12`; no local source mirror was required. - `chat_template.json`, `chat_template.jinja`, and `tokenizer_config.json["chat_template"]` were aligned for downstream compatibility checks. - Root-level `preprocessor_config.json` and `processor_config.json` are present intentionally for multimodal detection compatibility. ## Validation This artifact passed local validation in this workspace: - `mlx_vlm` prompt-packet validation: `PASS` - `vllm-mlx` OpenAI-compatible serve validation: `PASS` Local validation notes: - This family stayed on the original Track A packet; no ShowUI-style packet split was required. - Grounding returned the right object shape, but coordinates were not normalized to the requested `0-1000` grid. - Streamed answers drifted into Chinese on one serve-path check even though the non-stream answer stayed correct in English. ## Performance - Artifact size on disk: `15.47G` - Local fixed-packet `mlx_vlm` validation used about `18.12 GB` peak memory - Local `vllm-mlx` serve validation completed in about `22.14s` non-stream and `23.39s` streamed These are local validation measurements, not a full benchmark suite. ## Usage ### Install ```bash pip install -U mlx-vlm ``` ### CLI ```bash python -m mlx_vlm.generate \ --model mlx-community/GUI-Owl-7B-bf16 \ --image path/to/image.png \ --prompt "Describe the visible controls on this screen in five short bullet points." \ --max-tokens 256 \ --temperature 0.0 ``` ### Python ```python from mlx_vlm import load, generate model, processor = load("mlx-community/GUI-Owl-7B-bf16") result = generate( model, processor, prompt="Describe the visible controls on this screen in five short bullet points.", image="path/to/image.png", max_tokens=256, temp=0.0, ) print(result.text) ``` ### vllm-mlx Serve ```bash python -m vllm_mlx.cli serve mlx-community/GUI-Owl-7B-bf16 --mllm --localhost --port 8000 ``` ## Links - Upstream model: [mPLUG/GUI-Owl-7B](https://huggingface.co/mPLUG/GUI-Owl-7B) - Paper: [Mobile-Agent-v3: Foundamental Agents for GUI Automation](https://arxiv.org/abs/2508.15144) - Technical PDF: [Mobile-Agent-V3 Technical Report](https://github.com/X-PLUG/MobileAgent/blob/main/Mobile-Agent-v3/assets/MobileAgentV3_Tech.pdf) - GitHub: [X-PLUG/MobileAgent](https://github.com/X-PLUG/MobileAgent) - Base model lineage: [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) - MLX framework: [ml-explore/mlx](https://github.com/ml-explore/mlx) - mlx-vlm: [Blaizzy/mlx-vlm](https://github.com/Blaizzy/mlx-vlm) ## Other Quantizations Planned sibling repos in this wave: - [`mlx-community/GUI-Owl-7B-bf16`](https://huggingface.co/mlx-community/GUI-Owl-7B-bf16) - this model - [`mlx-community/GUI-Owl-7B-6bit`](https://huggingface.co/mlx-community/GUI-Owl-7B-6bit) ## Notes and Limitations - This card reports local MLX conversion and validation results only. - Upstream benchmark claims belong to the original GUI-Owl family and were not re-run here unless explicitly stated. - This family is better aligned to the Track A packet than ShowUI, but local validation still showed weak structured-action targeting and grounding normalization issues. - Streamed response quality can diverge from the non-stream path even when the serve path itself stays healthy. ## Citation If you use this MLX conversion, please also cite the original GUI-Owl work: ```bibtex @misc{ye2025mobileagentv3foundamentalagentsgui, title={Mobile-Agent-v3: Foundamental Agents for GUI Automation}, author={Jiabo Ye and Xi Zhang and Haiyang Xu and Haowei Liu and Junyang Wang and Zhaoqing Zhu and Ziwei Zheng and Feiyu Gao and Junjie Cao and Zhengxi Lu and Jitong Liao and Qi Zheng and Fei Huang and Jingren Zhou and Ming Yan}, year={2025}, eprint={2508.15144}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2508.15144}, } ``` ## License This repo follows the upstream model license: MIT. See the upstream model card for the authoritative license details: [mPLUG/GUI-Owl-7B](https://huggingface.co/mPLUG/GUI-Owl-7B).