Instructions to use mobileforge-anonymous/ForgeOwl-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mobileforge-anonymous/ForgeOwl-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="mobileforge-anonymous/ForgeOwl-8B")
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("mobileforge-anonymous/ForgeOwl-8B")
model = AutoModelForMultimodalLM.from_pretrained("mobileforge-anonymous/ForgeOwl-8B", 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 mobileforge-anonymous/ForgeOwl-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mobileforge-anonymous/ForgeOwl-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mobileforge-anonymous/ForgeOwl-8B",
		"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/mobileforge-anonymous/ForgeOwl-8B

SGLang

How to use mobileforge-anonymous/ForgeOwl-8B 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 "mobileforge-anonymous/ForgeOwl-8B" \
    --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": "mobileforge-anonymous/ForgeOwl-8B",
		"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 "mobileforge-anonymous/ForgeOwl-8B" \
        --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": "mobileforge-anonymous/ForgeOwl-8B",
		"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 mobileforge-anonymous/ForgeOwl-8B with Docker Model Runner:
```
docker model run hf.co/mobileforge-anonymous/ForgeOwl-8B
```

ForgeOwl-8B

ForgeOwl-8B is the GUI-Owl-1.5-8B-Instruct policy adapted with MobileForge on 900 automatically generated target-app tasks. MobileForge uses the policy's own rollouts, hierarchical critic feedback, corrective hints, and hint-contextualized step-level GRPO. No human-written adaptation tasks, demonstrations, or reward labels are used.

This repository is part of an anonymous ICLR submission artifact. Author and paper-identifying metadata will be added after review.

Anonymous project page: https://mobileforge-anonymous.github.io/

Evaluation

On AndroidWorld (116 tasks), this checkpoint obtains 78/116 (67.2%) Pass@1, 87/116 (75.0%) Pass@2, and 90/116 (77.6%) Pass@3. On the MobileWorld GUI-only split (117 tasks), it obtains 48/117 (41.0%) success rate.

Reproduction code and raw evaluation archives are available from the anonymous code repository and benchmark-results dataset.

Usage

Use the same loading and prompting interface as mPLUG/GUI-Owl-1.5-8B-Instruct. See the anonymous code repository for AndroidWorld and MobileWorld runners.

Limitations

The model can make incorrect or unsafe GUI actions. Run it only in isolated test environments, inspect actions before using it with personal data, and do not treat benchmark success as evidence of general reliability.

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Safetensors

Model size

9B params

Tensor type

BF16

Model tree for mobileforge-anonymous/ForgeOwl-8B

Base model

mPLUG/GUI-Owl-1.5-8B-Instruct

Finetuned

(4)

this model

Collection including mobileforge-anonymous/ForgeOwl-8B

MobileForge-Models

Collection

Model checkpoints for the anonymous MobileForge submission artifact. • 6 items • Updated about 8 hours ago