Instructions to use mustafaah/ScreenHighlighterRL-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mustafaah/ScreenHighlighterRL-2B with Transformers:

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

pipe = pipeline("image-text-to-text", model="mustafaah/ScreenHighlighterRL-2B")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("mustafaah/ScreenHighlighterRL-2B")
model = AutoModelForMultimodalLM.from_pretrained("mustafaah/ScreenHighlighterRL-2B", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mustafaah/ScreenHighlighterRL-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mustafaah/ScreenHighlighterRL-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mustafaah/ScreenHighlighterRL-2B",
		"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/mustafaah/ScreenHighlighterRL-2B

SGLang

How to use mustafaah/ScreenHighlighterRL-2B 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 "mustafaah/ScreenHighlighterRL-2B" \
    --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": "mustafaah/ScreenHighlighterRL-2B",
		"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 "mustafaah/ScreenHighlighterRL-2B" \
        --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": "mustafaah/ScreenHighlighterRL-2B",
		"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 mustafaah/ScreenHighlighterRL-2B with Docker Model Runner:
```
docker model run hf.co/mustafaah/ScreenHighlighterRL-2B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

ScreenHighlighterRL 2B

A screenshot and a request in; coloured bounding boxes out. The model highlights relevant content while the person stays in control. No clicking, typing, external OCR or inspection tools.

Full merged BF16 weights: final SFT + GRPO optimizer update 200, ready to load without PEFT or a separate adapter.

Get started / Chrome extension · Research article and 300-case viewer

Use

Install the community repository and run python -m screenhighlighter.server --model 2b. It downloads this release and serves the Chrome extension over localhost. See the repository for CUDA setup, a standalone screenshot command, remote-GPU tunnelling and memory options.

For direct Transformers use, load AutoProcessor and Qwen3VLForConditionalGeneration from mustafaah/ScreenHighlighterRL-2B. Use the exact system_prompt.txt, send one image and the instruction, and decode one completion with do_sample=False, max_new_tokens=1024. Use min_pixels=262144, max_pixels=1048576 and preserve image aspect ratio.

Output is {"op":"highlight","targets":[["yellow",x0,y0,x1,y1]]}. Coordinates are normalized to 0–1000 relative to the full original screenshot. Colours: yellow, green, blue, pink, purple, orange, red, white. Empty targets means no matches. White is an opaque mask.

Training and evaluation

800 SFT screenshots, 2,000 GRPO screenshots, 150 validation and 300 test cases; frozen vision tower, rank-32 / alpha-64 LoRA; 200 optimizer updates, 40 requests × 8 candidates/update, four 96 GB RTX PRO 6000 GPUs. Reward is deterministic same-colour one-to-one matching with boundary-tolerant precision/recall F1. Source adapter test reward: 0.519322. This is a geometric reward, not accuracy. These scores precede BF16 merging; no claim of a fresh merged-model benchmark.

Provenance and limits

See release-provenance.json, merge-validation.json and SHA256SUMS.json. BF16 adapter merging can slightly change outputs. The 2B merge was previously validated and is copied from pinned revision 19ae0731432822da915b5992104a2e50fed6f823; 4B merge includes tensor and logit checks. The model can miss or misidentify content. It sees only visible pixels; verify selections before consequential decisions. On-screen content is untrusted input.

Base model weights are Apache-2.0; retain Qwen's attribution. Screenshot examples retain their respective source rights. Author: Mustafa Hussain.

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Safetensors

Model size

2B params

Tensor type

BF16

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