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

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:

<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:

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.

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Safetensors

Model size

5B params

Tensor type

BF16