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

Libraries

How to use PhoneBuddyAI/PhoneBuddy-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="PhoneBuddyAI/PhoneBuddy-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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("PhoneBuddyAI/PhoneBuddy-4B")
model = AutoModelForMultimodalLM.from_pretrained("PhoneBuddyAI/PhoneBuddy-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?"}
        ]
    },
]
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 PhoneBuddyAI/PhoneBuddy-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PhoneBuddyAI/PhoneBuddy-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": "PhoneBuddyAI/PhoneBuddy-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/PhoneBuddyAI/PhoneBuddy-4B

SGLang

How to use PhoneBuddyAI/PhoneBuddy-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 "PhoneBuddyAI/PhoneBuddy-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": "PhoneBuddyAI/PhoneBuddy-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 "PhoneBuddyAI/PhoneBuddy-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": "PhoneBuddyAI/PhoneBuddy-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 PhoneBuddyAI/PhoneBuddy-4B with Docker Model Runner:
```
docker model run hf.co/PhoneBuddyAI/PhoneBuddy-4B
```

PhoneBuddy-4B / README.md

XGGNet

Update loading environment notes

33da1a8 verified 20 days ago

preview code

Raw

History Blame Contribute Delete

2.3 kB

	---
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- vision-language
	- qwen3.5-vl
	- phone-agent
	- tool-use
	---

	# PhoneBuddy-4B

	PhoneBuddy-4B is the main PhoneBuddy Real+Mock reinforcement-learning checkpoint.

	Project page: https://phonebuddyai.github.io/

	GitHub: https://github.com/PhoneBuddyAI/phonebuddy

	## Model Details

	- Model family: Qwen3.5 VL style checkpoint
	- `model_type`: `qwen3_5`
	- Processor: `Qwen3VLProcessor`
	- Checkpoint role: main Real+Mock RL checkpoint
	- Tool-call format: Qwen-style XML as defined in `chat_template.jinja`

	The model card and repository are initially published as private for validation.

	## Tool-Call Format

	PhoneBuddy-4B follows the Qwen-style XML tool-call format defined by the bundled `chat_template.jinja`, for example:

	```xml
	<tool_call>
	<function=example_function_name>
	<parameter=example_parameter_1>
	value_1
	</parameter>
	</function>
	</tool_call>
	```

	Use the tokenizer or processor chat template from this repository when constructing prompts with tools.

	## Loading Environment

	These checkpoints use Qwen3.5 VL style model metadata:

	- `model_type`: `qwen3_5`
	- Architecture: `Qwen3_5ForConditionalGeneration`
	- Processor: `Qwen3VLProcessor`
	- Tokenizer metadata: `TokenizersBackend`

	Use the matching Qwen3.5 VL / PhoneBuddy training or inference environment that registers these classes. In a generic public Transformers environment, compatibility depends on whether that build includes `qwen3_5` and the tokenizer backend used by this checkpoint.

	A minimal processor load can be tested with:

	```python
	from transformers import AutoProcessor

	repo_id = "PhoneBuddyAI/PhoneBuddy-4B"
	processor = AutoProcessor.from_pretrained(repo_id, trust_remote_code=False)
	```

	Full config, tokenizer, and model loading should be done in an environment that supports the classes above. For example, public `transformers==4.57.6` does not register `model_type=qwen3_5`, and `AutoTokenizer` does not import `TokenizersBackend`; in that environment those failures indicate version/class compatibility, not missing checkpoint files.

	## Intended Use

	PhoneBuddy is designed for research on phone agents, multimodal tool use, and visual action reasoning. See the project page and GitHub repository for code and usage details.