thealper2/smollm2-135m-android-control

smollm2-135m-android-control

LoRA SFT of HuggingFaceTB/SmolLM2-135M-Instruct as a text-only Android UI action policy. Input: instruction + textual UI element list + action history. Output: one action as a single-line JSON object. The model does not consume images.

Output schema

Normalized coordinates: x = round(px / screen_width * 1000), same for y. Element bounds in the prompt use the same 0-1000 grid.

Prompt format

Chat template of the base tokenizer, system + user turn, greedy decoding.

System:

You are an Android UI control agent. Given the instruction and the current screen, output the single next action as raw JSON and nothing else.
Actions: {"action_type":"click","target":N} | {"action_type":"long_press","target":N} | {"action_type":"input_text","text":"..."} | {"action_type":"scroll","direction":"up|down|left|right"} | {"action_type":"open_app","app_name":"..."} | {"action_type":"wait"} | {"action_type":"navigate_back"} | {"action_type":"navigate_home"}
N is an element index from the UI list. If no element fits, use {"action_type":"click","x":X,"y":Y} with X and Y in 0-1000.

User:

Goal: <episode goal>
Instruction: <step instruction>
Current Android UI:
screen 1080x2400 app=com.android.settings
[0] EditText "Search settings" [56,108,944,167] click,edit
[1] TextView "Network & internet" [56,200,944,267] click
...
Previous actions:
1. open_app "Settings"
Return exactly one action.

Element line: [index] class "label" [left,top,right,bottom] flags. Label = text > content-description > hint. History entries reference element labels, not indices.

Data

• —Source: OfficerChul/Android-Control-84k @ 0248027f747c9d57bd09c14e8f044f9a8103dddd (step instructions, actions, screenshot names).
• —UI text: accessibility trees from the original AndroidControl TFRecords (gs://gresearch/android_control), joined by episode id and step.
• —Element selection: visible, enabled, labelled or interactive nodes; status bar removed; top 32 by score, reading order; labels truncated to 40 chars.
• —Click/long-press target: smallest kept element containing the gold point; otherwise normalized coordinates.
• —History: last 5 actions.
• —Upstream rows (train+test): 83,848; converted: 83,848; discarded: 0
• —Rows with an accessibility tree: 83,848; without: 0
• —Position encoding: coords 5,919, none 31,763, target 46,166
• —Token budget: 1280 (prompt+completion); rows with elements dropped to fit: 15,889
• —Train rows from episodes that also occur in the upstream test split removed: 5,642

Training

Evaluation

Split: test (n=904), identical prompts for both models, greedy decoding. Strict JSON = the completion is exactly one schema-valid JSON object. Click target accuracy requires the correct action type and index. Coordinate accuracy: L∞ distance ≤ 50 on the 0-1000 grid.

Per-action exact match:

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "thealper2/smollm2-135m-android-control"
model = AutoModelForCausalLM.from_pretrained(repo)
tok = AutoTokenizer.from_pretrained(repo)

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},  # see "Prompt format"
    {"role": "user", "content": user_prompt},
]
text = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tok(text, return_tensors="pt", add_special_tokens=False)
out = model.generate(**inputs, max_new_tokens=48, do_sample=False)
print(tok.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
# {"action_type":"click","target":1}

Limitations

• —Text-only policy: no pixels. Elements absent from the accessibility tree (canvas, games, some WebViews) are not visible to it.
• —Target indices are only meaningful for the element list they were predicted on; the same element selection rules must be used at inference.
• —Training data has no navigate_home examples and only 42 long_press examples; the test split is class-balanced, so those classes dominate the error.
• —open_app emits an app display name; mapping to a package is done by the executor.
• —Single-step accuracy on offline data does not measure closed-loop task success.