GUI-Swift-Mobile-2B

Single-Pass Mobile GUI Action Prediction
Results · Examples · Quick start · 简体中文

GUI-Swift-Mobile-2B turns a screenshot, the current step instruction and executed-action history into one structured Android action through one native causal forward over 105 masked value slots. Built on GUI-Owl-1.5-2B-Instruct, it continues the GUI-Swift-4B family with an Android-focused protocol.

Release: AndroidControl SFT · two epochs · step4658
Download: unchanged PEFT LoRA adapter · 433.68M adapter parameters · 1.73 GB
Protocol: 105 slots · 256 coordinate bins per axis · 16K context

The base model is downloaded separately. Use the included native inference runner: ordinary autoregressive generate() does not implement this trained protocol. Single-pass describes the mechanism; no measured speedup or live task-success advantage is claimed.

Results

ScreenSpot-v2 includes all 501 mobile rows (Android, iOS and shop source labels); Android-only source rows score 156/211 (73.93%). Text-widget grounding is 233/290 (80.34%), icon grounding 116/211 (54.98%). We reuse the inclusive point-in-box expression from pinned SeeClick 0ef37ac4d7aaf37ba7b990e3e3c3ca77e1fb8f93, require a decoded tap, and count other actions as misses. Original labels are retained, including reported source-label issues.

AndroidControl uses all 8,423 admitted held-out test steps, low-level step instructions, and reference executed-action history with the same whole-turn window used in training. Train/validation/test trajectory IDs are disjoint. This is offline teacher-forced next-action prediction, not rollouts or an official AndroidControl leaderboard result. Source conversion excludes unsupported or invalid source rows; these counts describe our admitted split, not every raw dataset row.

Tolerance match requires the same action and field set, each coordinate axis within 50/1000, text normalized Levenshtein similarity ≥0.9, and other parameters exact. The tolerance is a project reporting convention chosen earlier during development, not an established task-success metric. Strict equality is reported alongside it. Machine-readable results include action-level breakdowns and hashes.

The 690-step development validation scored action 669/690 (96.96%), strict action 209/690 (30.29%), text exact 43/98 (43.88%), and loss 4.064669. Text exact match is substantially lower on the held-out test split; text spelling, omission/repetition, icons, and some scroll directions remain limitations. No AndroidWorld score or end-to-end phone success rate is claimed. Base-model pretraining overlap with evaluation data is unknown.

Real validation examples

Four actual saved step4658 predictions, selected after evaluation. The current input screenshot is shown; prior observations were also supplied where noted. The gallery includes a text failure and is not a performance estimate or proof of an executed task. Images and outputs are unchanged, with source IDs and hashes.

Quick start

Verified with Python 3.10, CUDA and a 24 GB RTX 4090. Download this repository, then use an isolated environment and the official CUDA-enabled Torch wheel appropriate for your machine:

hf download dongjintao/GUI-Swift-Mobile-2B --local-dir GUI-Swift-Mobile-2B
cd GUI-Swift-Mobile-2B
pip install -r requirements.txt
python prepare_dependencies.py --destination /path/on/data-disk/gui-swift-mobile-deps
python infer.py --base-model /path/on/data-disk/gui-swift-mobile-deps/GUI-Owl-1.5-2B-Instruct \
  --bundle /path/on/data-disk/gui-swift-mobile-deps/runtime-bundle \
  --request request.json

request.json uses screenshot paths relative to that file:

{"turns":[{"instruction":"Tap Settings.","image":"screen.png","receipt":null}]}

For the next observation, append a turn with the current instruction, screenshot and actually executed previous action/outcome:

{"instruction":"Open Wi-Fi settings.","image":"screen2.png","receipt":{"executed_action":{"action":"tap","coordinate":[500,300]},"outcome":"unverified"}}

Receipt and decoded coordinates are normalized to 0–1000. The runner returns an action; it does not execute it or verify task success. Earlier assistant MASK tables remain as input scaffolds. Oldest complete turns are removed only when needed to fit 16,384 tokens. This runner recomputes the retained conversation; it does not implement persistent vLLM KV caching.

The pinned GUI-Decisions dependency is fetched separately because its selected snapshot contains no LICENSE file; it is not redistributed here. You can supply an existing checkout with --gui-decisions and an existing base with --base-model. See THIRD_PARTY.md.

Protocol and training

Slots: 1 action + 4 coordinate values + 100 text positions. Coordinates use 256 bins per axis. Text supports up to 99 native payload tokens followed by EOS; inactive fields are ignored. Action codes A–P cover tap, long_press, swipe, scroll_up/down/left/right, type_text, open_app, back, home, enter, menu, wait, done and failed. Protocol support does not imply that every action has training coverage.

AndroidControl: 74,525 admitted training targets, 690 validation steps, 8,423 test steps. Two logical epochs, 4,658 updates, effective batch 32 with smaller epoch tails, two RTX 4090s. The official LLaMA-Factory loop is reused through a local fixed-slot adapter. This is SFT, not RL.

Official PEFT LoRA: rank256 / alpha16 / dropout0.05 / learning rate1e-5, adapting 301 linear modules across vision, projections, language and output head. Embeddings, normalization and convolutional patch projection are excluded. Frozen tied input/output weights stay shared; the output head has a separate LoRA delta. Do not blindly merge into tied embeddings.

The Desktop131 objective is L_action + 2 * mean(active parameter-field losses) (action loss alone for parameter-free actions), averaged over samples. Coordinate soft targets use sigma0.005 and full-vocabulary normalization. Base precision is BF16; adapters remain FP32. Image bounds: 65,536–1,048,576 pixels per observation. The adapter JSON omits optional task_type:null, retaining PEFT's default generic wrapper.

Attribution and reproducibility

GUI-Owl is the base model, not the author of this independent derivative. GUI-Decisions primarily inspired our 256-bin localization representation/readout. Single-forward complete-action prediction was already part of our project. Concrete reuse of its coordinate labels, targets and native slot reader is credited separately in THIRD_PARTY.md.

Base revision: 528ceaec795bbfbe6103bd79e03db849feadfb24. Adapter SHA256: 7312466ddb1f9a1137de4a2e3b0bd311a3f7662b64331b0cb2dd5ac33636aa0c.

Release validation compares the portable runner against frozen inference; provenance, configuration and checksums pin the package. We publish four curated validation screenshots, not the raw corpus, private conversations, optimizer states or credentials.