Instructions to use emirozcan16/AA-VL-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use emirozcan16/AA-VL-4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="emirozcan16/AA-VL-4B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("emirozcan16/AA-VL-4B")
model = AutoModelForMultimodalLM.from_pretrained("emirozcan16/AA-VL-4B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use emirozcan16/AA-VL-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "emirozcan16/AA-VL-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "emirozcan16/AA-VL-4B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/emirozcan16/AA-VL-4B

SGLang

How to use emirozcan16/AA-VL-4B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "emirozcan16/AA-VL-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "emirozcan16/AA-VL-4B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "emirozcan16/AA-VL-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "emirozcan16/AA-VL-4B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use emirozcan16/AA-VL-4B with Docker Model Runner:
```
docker model run hf.co/emirozcan16/AA-VL-4B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

AA-VL-4B — Data-Efficient GUI Grounding (4B)

~7K curated training rows · ~$8 compute budget · 100% verifiable per-sample predictions included

📦 GGUF Quants (llama.cpp / LM Studio) · 🎯 Interactive OSWorld-G Prediction Viewer · 🧩 LoRA Adapter (PEFT) · 🔍 VERIFY.md & Raw JSONL

AA-VL-4B is a 4.44B GUI grounding model (a r=32 LoRA adapter merged into Qwen/Qwen3-VL-4B-Instruct) that maps a desktop/web/mobile screenshot and a natural-language instruction to a single click coordinate (click x=<int> y=<int> on a 0–999 normalized grid) or emits refuse when the target element is absent.

AA-VL-4B Raw Greedy Pass@1 Predictions on OSWorld-G

Highlights

Strict Raw Pass@1 Evaluation: Single greedy prediction (do_sample=False), no image cropping, no iterative zoom, no planner, no pass@k, no reranking.
Extreme Data & Compute Efficiency: Trained on ~7K curated samples (60% click, ~40% refusal) on a **$8 cloud GPU budget** (initial QLoRA 4-bit adapter followed by 250 steps in fp16).
Zero-Trust Verifiability: Unlike closed evaluations, this repository ships the complete per-sample output logs (osworld_g_predictions.jsonl — 1,128 rows; predictions.jsonl — 5,479 rows) containing the exact prompt, raw model text, normalized and pixel coordinates, ground-truth bounding boxes/polygons, and SHA-256 checksums so anyone can recompute the scores offline in 2 seconds.

Benchmark Results & Reference Context

All AA-VL-4B scores below are computed from the shipped fp16 merged weights (model.safetensors) in a single greedy pass:

Benchmark	AA-VL-4B (Ours, 4B)	Correct / Total	Reference Context (Published Numbers)*
OSWorld-G (Original)	65.25%	`368 / 564`	Official Leaderboard: GUI-Owl-7B (`55.9`), UI-Venus-1.0-7B (`54.6`), Jedi-7B (`54.1` on 4M samples), UI-TARS-1.5-7B (`52.8`), Qwen2.5-VL-7B (`31.4`). Recent 8B tech reports: MAI-UI-8B (`60.1`), UI-Venus-1.5-8B (`69.7`).
OSWorld-G (Refined)	74.47%	`420 / 564`	Published 7B–8B reports: UI-Venus-1.0-7B (`61.7`), UI-Venus-1.5-8B (`74.1`).
UI-Vision (Final Pooled)	29.93%	`1640 / 5479`	Official Leaderboard: UI-TARS-72B (`25.50`). Self-reported ≤8B: UI-Venus-1.0-7B (`26.5`), Phi-Ground (`27.2`), MAI-UI-2B (`30.3`), MAI-UI-8B (`40.7`), UI-Venus-1.5-8B (`46.5`).

UI-Vision Subset Breakdown: Basic 39.28% (696/1772) · Functional 37.75% (669/1772) · Spatial 14.21% (275/1935).
*Note on Reference Context: Published numbers for third-party models come from their respective papers/leaderboards and may differ in system prompt, resolution scaling, or evaluation harness. We include them solely to provide scale context for what a 4B model trained on ~7K samples achieves. Our own runs are 100% raw greedy pass@1 and fully auditable via the included .jsonl files.

Instant Verification (No GPU Required)

Every raw generation, coordinate, and ground-truth box ships directly in this repo:

Benchmark	Artifact File	Rows	Score
OSWorld-G (Original / Refined)	`osworld_g_predictions.jsonl`	1,128 (564 × 2)	368/564 (65.25%) / 420/564 (74.47%)
UI-Vision Final	`predictions.jsonl`	5,479	1640/5479 (29.93%)

Re-score both benchmarks from scratch in one line:

python3 -c "import json;r=[json.loads(l) for l in open('osworld_g_predictions.jsonl')];print({v:sum(a['correct'] for a in r if a['variant']==v) for v in ('original','refined')})"
python3 -c "import json;r=[json.loads(l) for l in open('predictions.jsonl')];print(sum(x['correct'] for x in r),'/',len(r))"

See VERIFY.md and osworld_g_eval.py for full protocol details, coordinate mapping (pred_xy_px = pred_xy_norm999 / 1000 * screen_size), and SHA-256 hashes. You can also browse predictions visually in the Interactive OSWorld-G Demo Space.

Usage (`transformers`)

from transformers import AutoProcessor, AutoModelForImageTextToText
from PIL import Image

MODEL = "emirozcan16/AA-VL-4B"
proc = AutoProcessor.from_pretrained(MODEL, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
    MODEL, trust_remote_code=True, torch_dtype="auto", device_map="auto"
)

img = Image.open("screen.png").convert("RGB")
msgs = [{"role": "user", "content": [
    {"type": "image", "image": img},
    {"type": "text",
     "text": "Instruction: Click the funnel icon right of the search bar. "
             "Reply with the single action line now, nothing else:"}
]}]
txt = proc.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
inp = proc(text=[txt], images=[img], return_tensors="pt").to(model.device)
out = model.generate(**inp, max_new_tokens=64, do_sample=False)
print(proc.batch_decode(out[:, inp["input_ids"].shape[1]:], skip_special_tokens=True)[0])
# -> click x=<int> y=<int>   (0-999 normalized scale)   or   refuse

For the exact system prompt used in the benchmark runs, see SYS_CLICK in osworld_g_eval.py.

GGUF (`llama.cpp` / LM Studio) & LoRA Adapter

GGUF Quantizations (Q2_K to F16 + mmproj.gguf): emirozcan16/AA-VL-4B-GGUF (and community static quants at mradermacher/AA-VL-4B-GGUF)
PEFT LoRA Adapter (r=32, alpha=64): emirozcan16/AA-VL-4B-adapter

Training Data

~~7K curated rows (~~60% click, ~40% refusal). Sources:

Source	What we used	Role	License
Jedi (`xlangai/Jedi`, HF)	icon/component grounding clicks (`train_raw`/`val_raw`, `os_layout`, component libraries)	click labels	see upstream
GroundCUA	GUI grounding click slice (~2.5K rows, `picked_json` + images)	click labels	MIT
ScreenSpot-v2 (`OS-Copilot/ScreenSpot-v2`, HF)	desktop screenshots only	build synthetic refusal pairs	see upstream
Our synthesis	instruction ↔️ unrelated screenshot cross-pairing (doc–doc, icon–icon, desktop)	refusal examples	ours

Base model: Qwen/Qwen3-VL-4B-Instruct (Apache-2.0). No private or scraped user data. The assembled training set is not released for now. We did not train on any benchmark's answer labels.

Eval Protocol & Limitations

OSWorld-G: official point-in-box / point-in-polygon (510 click + 54 refusal = 564 samples), evaluated on both Original (OSWorld-G.json) and Refined (OSWorld-G_refined.json) instructions.
UI-Vision: official harness over its three subsets (5,479 samples total).
Limitations: Coordinates are emitted on a 0–999 normalized grid. Single seed; benchmarks have not yet been independently reproduced by third parties (which is why all raw per-sample predictions are shipped in the repo).

Project

Built in ~3 months on a ~$8 compute budget. Contact: ozcannemir08@gmail.com

Downloads last month: 97

Safetensors

Model size

4B params

Tensor type

F16

Model tree for emirozcan16/AA-VL-4B

Base model

Qwen/Qwen3-VL-4B-Instruct

Finetuned

(520)

this model

Quantizations

2 models

emirozcan16
/

AA-VL-4B

AA-VL-4B — Data-Efficient GUI Grounding (4B)

Highlights

Benchmark Results & Reference Context

Instant Verification (No GPU Required)

Usage (`transformers`)

GGUF (`llama.cpp` / LM Studio) & LoRA Adapter

Training Data

Eval Protocol & Limitations

Project

Model tree for emirozcan16/AA-VL-4B

Space using emirozcan16/AA-VL-4B 1

AA-VL-4B — Data-Efficient GUI Grounding (4B)

Highlights

Benchmark Results & Reference Context

Instant Verification (No GPU Required)

Usage (transformers)

GGUF (llama.cpp / LM Studio) & LoRA Adapter

Training Data

Eval Protocol & Limitations

Project

Model tree for emirozcan16/AA-VL-4B

Space using emirozcan16/AA-VL-4B 1

Usage (`transformers`)

GGUF (`llama.cpp` / LM Studio) & LoRA Adapter