Instructions to use PhoneBuddyAI/PhoneBuddy-0.8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PhoneBuddyAI/PhoneBuddy-0.8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="PhoneBuddyAI/PhoneBuddy-0.8B") 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("PhoneBuddyAI/PhoneBuddy-0.8B") model = AutoModelForMultimodalLM.from_pretrained("PhoneBuddyAI/PhoneBuddy-0.8B") 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?"} ] }, ] 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-0.8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PhoneBuddyAI/PhoneBuddy-0.8B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PhoneBuddyAI/PhoneBuddy-0.8B", "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/PhoneBuddyAI/PhoneBuddy-0.8B
- SGLang
How to use PhoneBuddyAI/PhoneBuddy-0.8B 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 "PhoneBuddyAI/PhoneBuddy-0.8B" \ --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": "PhoneBuddyAI/PhoneBuddy-0.8B", "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 "PhoneBuddyAI/PhoneBuddy-0.8B" \ --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": "PhoneBuddyAI/PhoneBuddy-0.8B", "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 PhoneBuddyAI/PhoneBuddy-0.8B with Docker Model Runner:
docker model run hf.co/PhoneBuddyAI/PhoneBuddy-0.8B
PhoneBuddy-0.8B
PhoneBuddy-0.8B is the small 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: small 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-0.8B 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-0.8B"
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. This smaller checkpoint is intended for lower-cost experiments and comparisons.
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