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
```

Link model card to paper and add citation

by nielsr HF Staff - opened 13 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+20

-4

Files changed (1) hide show

README.md +20 -4

README.md CHANGED Viewed

@@ -2,16 +2,18 @@
 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
@@ -67,3 +69,17 @@ Full config, tokenizer, and model loading should be done in an environment that
 ## 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.

 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.
+This model is presented in the paper [PhoneBuddy: Training Open Models for Agentic Phone Use](https://huggingface.co/papers/2606.23049).
 Project page: https://phonebuddyai.github.io/
 GitHub: https://github.com/PhoneBuddyAI/phonebuddy
 ## 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.
+## Citation
+```bibtex
+@misc{tang2026phonebuddytrainingopenmodels,
+      title={PhoneBuddy: Training Open Models for Agentic Phone Use},
+      author={Zhengyang Tang and Xin Lai and Pengyuan Lyu and Xinyuan Wang and Tianyi Bai and Chenxin Li and Yiduo Guo and Huawen Shen and Yuxuan Liu and Junyi Li and Zhengyao Fang and Yang Ding and Yi Zhang and Weinong Wang and Xingran Zhou and Liang Wu and Fei Tang and Sunqi Fan and Shangpin Peng and Zheng Ruan and Anran Zhang and Benyou Wang and Ji-Rong Wen and Rui Yan and Chengquan Zhang and Han Hu},
+      year={2026},
+      eprint={2606.23049},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2606.23049},
+}
+```