Image-Text-to-Text
Transformers
Diffusers
Safetensors
qwen3_vl
vision-language-model
image-decomposition
conversational
Instructions to use SynLayers/Bbox-caption-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SynLayers/Bbox-caption-8b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="SynLayers/Bbox-caption-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, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("SynLayers/Bbox-caption-8b") model = AutoModelForImageTextToText.from_pretrained("SynLayers/Bbox-caption-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
- vLLM
How to use SynLayers/Bbox-caption-8b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SynLayers/Bbox-caption-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": "SynLayers/Bbox-caption-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/SynLayers/Bbox-caption-8b
- SGLang
How to use SynLayers/Bbox-caption-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 "SynLayers/Bbox-caption-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": "SynLayers/Bbox-caption-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 "SynLayers/Bbox-caption-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": "SynLayers/Bbox-caption-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 SynLayers/Bbox-caption-8b with Docker Model Runner:
docker model run hf.co/SynLayers/Bbox-caption-8b
File size: 3,832 Bytes
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channels:
- defaults
dependencies:
- _libgcc_mutex=0.1=main
- _openmp_mutex=5.1=1_gnu
- bzip2=1.0.8=h5eee18b_6
- ca-certificates=2025.7.15=h06a4308_0
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- xorg-xorgproto=2024.1=h5eee18b_1
- xz=5.6.4=h5eee18b_1
- zlib=1.2.13=h5eee18b_1
- pip:
- absl-py==2.3.1
- accelerate==0.34.2
- addict==2.4.0
- aiohappyeyeballs==2.6.1
- aiohttp==3.12.15
- aiosignal==1.4.0
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- charset-normalizer==3.4.3
- click==8.2.1
- contourpy==1.3.2
- cycler==0.12.1
- datasets==2.20.0
- deepspeed==0.17.5
- diffusers==0.31.0
- dill==0.3.8
- docker-pycreds==0.4.0
- einops==0.8.0
- filelock==3.19.1
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- gitdb==4.0.12
- gitpython==3.1.45
- grpcio==1.74.0
- hf-xet==1.1.8
- hjson==3.1.0
- huggingface-hub==0.34.4
- idna==3.10
- imageio==2.37.0
- importlib-metadata==8.7.0
- jinja2==3.1.6
- joblib==1.5.2
- kiwisolver==1.4.9
- lazy-loader==0.4
- markdown==3.9
- markdown-it-py==4.0.0
- markupsafe==3.0.2
- matplotlib==3.10.5
- mdurl==0.1.2
- mmengine==0.10.4
- mpmath==1.3.0
- msgpack==1.1.1
- multidict==6.6.4
- multiprocess==0.70.16
- networkx==3.4.2
- ninja==1.13.0
- numpy==2.2.6
- nvidia-cublas-cu12==12.1.3.1
- nvidia-cuda-cupti-cu12==12.1.105
- nvidia-cuda-nvrtc-cu12==12.1.105
- nvidia-cuda-runtime-cu12==12.1.105
- nvidia-cudnn-cu12==9.1.0.70
- nvidia-cufft-cu12==11.0.2.54
- nvidia-curand-cu12==10.3.2.106
- nvidia-cusolver-cu12==11.4.5.107
- nvidia-cusparse-cu12==12.1.0.106
- nvidia-ml-py==13.580.65
- nvidia-nccl-cu12==2.20.5
- nvidia-nvjitlink-cu12==12.9.86
- nvidia-nvtx-cu12==12.1.105
- opencv-python==4.12.0.88
- packaging==25.0
- pandas==2.3.2
- peft==0.12.0
- pillow==11.3.0
- platformdirs==4.3.8
- prodigyopt==1.0
- propcache==0.3.2
- protobuf==5.29.5
- psutil==7.0.0
- py-cpuinfo==9.0.0
- pyarrow==21.0.0
- pyarrow-hotfix==0.7
- pydantic==2.11.7
- pydantic-core==2.33.2
- pygments==2.19.2
- pyparsing==3.2.3
- python-dateutil==2.9.0.post0
- pytz==2025.2
- pyyaml==6.0.2
- regex==2025.7.34
- requests==2.32.5
- rich==14.1.0
- qwen-vl-utils==0.0.11
- safetensors==0.6.2
- scikit-image==0.25.2
- scikit-learn==1.7.2
- scipy==1.15.3
- sentencepiece==0.2.0
- sentry-sdk==2.35.0
- setproctitle==1.3.6
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- torch==2.4.0
- torchvision==0.19.0
- tqdm==4.67.1
- transformers==4.44.0
- triton==3.0.0
- typing-extensions==4.14.1
- typing-inspection==0.4.1
- tzdata==2025.2
- urllib3==2.5.0
- wandb==0.17.7
- werkzeug==3.1.3
- wheel==0.45.1
- xxhash==3.5.0
- yapf==0.43.0
- yarl==1.20.1
- zipp==3.23.0
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