Instructions to use OpenMobile-2/OpenMobile-2-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenMobile-2/OpenMobile-2-27B with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenMobile-2/OpenMobile-2-27B")
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("OpenMobile-2/OpenMobile-2-27B")
model = AutoModelForMultimodalLM.from_pretrained("OpenMobile-2/OpenMobile-2-27B", 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 OpenMobile-2/OpenMobile-2-27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenMobile-2/OpenMobile-2-27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenMobile-2/OpenMobile-2-27B",
		"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/OpenMobile-2/OpenMobile-2-27B

SGLang

How to use OpenMobile-2/OpenMobile-2-27B 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 "OpenMobile-2/OpenMobile-2-27B" \
    --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": "OpenMobile-2/OpenMobile-2-27B",
		"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 "OpenMobile-2/OpenMobile-2-27B" \
        --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": "OpenMobile-2/OpenMobile-2-27B",
		"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 OpenMobile-2/OpenMobile-2-27B with Docker Model Runner:
```
docker model run hf.co/OpenMobile-2/OpenMobile-2-27B
```

yanhhh commited on 13 days ago

Commit

e78ea19

verified ·

1 Parent(s): 8a9cf84

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -14

README.md CHANGED Viewed

@@ -13,7 +13,7 @@ tags:
 # Introduction
-OpenMobile-2-27B is a mobile GUI agent fine-tuned from [Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B). It completes Android tasks from screenshots, and can call app-native tools when the foreground app exposes them.
 ## Results
@@ -29,10 +29,16 @@ OpenMobile-2-27B is a mobile GUI agent fine-tuned from [Qwen3.6-27B](https://hug
 ## Deploy
-Two GPUs, context length 65536. OpenAI-compatible API on port 8000. Eight GPUs: set tensor parallel to 8 and context length to 262144.
 **vLLM**
 ```bash
 vllm serve OpenMobile-2/OpenMobile-2-27B \
   --tensor-parallel-size 2 \
@@ -45,18 +51,6 @@ vllm serve OpenMobile-2/OpenMobile-2-27B \
   --gdn-prefill-backend triton
 ```
-**SGLang**
-```bash
-python -m sglang.launch_server \
-  --model-path OpenMobile-2/OpenMobile-2-27B \
-  --tp-size 2 \
-  --mem-fraction-static 0.8 \
-  --context-length 65536 \
-  --reasoning-parser qwen3 \
-  --tool-call-parser qwen3_coder \
-  --enable-multimodal
-```
 ## Citation

 # Introduction
+OpenMobile-2-27B is a mobile GUI agent fine-tuned from [Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) on [OpenMobile-Data-v2](https://huggingface.co/datasets/OpenMobile-2/OpenMobile-Data-v2). It completes Android tasks from screenshots, and can call app-native tools when the foreground app exposes them.
 ## Results
 ## Deploy
+The commands below use a context length of 65536 and tensor parallel 2.
 **vLLM**
+We use vLLM 0.19.1.
+```bash
+pip install vllm==0.19.1
+```
 ```bash
 vllm serve OpenMobile-2/OpenMobile-2-27B \
   --tensor-parallel-size 2 \
   --gdn-prefill-backend triton
 ```
 ## Citation