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
Upload demo/upload_used_bundle_to_hf.py with huggingface_hub
Browse files- demo/upload_used_bundle_to_hf.py +112 -0
demo/upload_used_bundle_to_hf.py
ADDED
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from __future__ import annotations
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import argparse
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import os
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from pathlib import Path
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from huggingface_hub import HfApi
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PROJECT_ROOT = Path(__file__).resolve().parents[1]
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SHARE_ROOT = Path("/project/llmsvgen/share/data/kmw_layered_checkpoint")
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USED_FILE_MAP = {
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PROJECT_ROOT / "demo" / "__init__.py": "demo/__init__.py",
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PROJECT_ROOT / "demo" / "app.py": "demo/app.py",
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PROJECT_ROOT / "demo" / "hf_repo_assets.py": "demo/hf_repo_assets.py",
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PROJECT_ROOT / "demo" / "README.md": "demo/README.md",
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PROJECT_ROOT / "demo" / "publish_space.py": "demo/publish_space.py",
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PROJECT_ROOT / "demo" / "real_world_pipeline.py": "demo/real_world_pipeline.py",
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PROJECT_ROOT / "demo" / "requirements-hf-space.txt": "demo/requirements-hf-space.txt",
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PROJECT_ROOT / "demo" / "upload_used_bundle_to_hf.py": "demo/upload_used_bundle_to_hf.py",
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PROJECT_ROOT / "demo" / "infer" / "__init__.py": "demo/infer/__init__.py",
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PROJECT_ROOT / "demo" / "infer" / "run_caption_bbox_infer.py": "demo/infer/run_caption_bbox_infer.py",
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PROJECT_ROOT / "demo" / "infer" / "vlm_bbox_inference.py": "demo/infer/vlm_bbox_inference.py",
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PROJECT_ROOT / "infer" / "__init__.py": "infer/__init__.py",
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PROJECT_ROOT / "infer" / "common_infer.py": "infer/common_infer.py",
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PROJECT_ROOT / "infer" / "infer.py": "infer/infer.py",
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PROJECT_ROOT / "infer" / "infer.yaml": "infer/infer.yaml",
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PROJECT_ROOT / "models" / "__init__.py": "models/__init__.py",
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PROJECT_ROOT / "models" / "multiLayer_adapter.py": "models/multiLayer_adapter.py",
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PROJECT_ROOT / "models" / "mmdit.py": "models/mmdit.py",
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PROJECT_ROOT / "models" / "pipeline.py": "models/pipeline.py",
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PROJECT_ROOT / "models" / "transp_vae.py": "models/transp_vae.py",
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PROJECT_ROOT / "tools" / "__init__.py": "tools/__init__.py",
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PROJECT_ROOT / "tools" / "tools.py": "tools/tools.py",
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PROJECT_ROOT / "environment.yml": "environment.yml",
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}
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REQUIRED_ASSET_FILES = {
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PROJECT_ROOT / "ckpt" / "trans_vae" / "0008000.pt": "ckpt/trans_vae/0008000.pt",
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PROJECT_ROOT / "ckpt" / "pre_trained_LoRA" / "pytorch_lora_weights.safetensors": "ckpt/pre_trained_LoRA/pytorch_lora_weights.safetensors",
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PROJECT_ROOT / "ckpt" / "prism_ft_LoRA" / "pytorch_lora_weights.safetensors": "ckpt/prism_ft_LoRA/pytorch_lora_weights.safetensors",
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}
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REQUIRED_ASSET_FOLDERS = {
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SHARE_ROOT / "SynLayers_checkpoints" / "FLUX.1-dev": "SynLayers_checkpoints/FLUX.1-dev",
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SHARE_ROOT / "SynLayers_ckpt" / "step_120000": "SynLayers_ckpt/step_120000",
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SHARE_ROOT / "SynLayers_checkpoints" / "FLUX.1-dev-Controlnet-Inpainting-Alpha": "SynLayers_checkpoints/FLUX.1-dev-Controlnet-Inpainting-Alpha",
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}
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def upload_files(api: HfApi, repo_id: str, repo_type: str):
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for local_path, remote_path in USED_FILE_MAP.items():
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if not local_path.exists():
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print(f"Skipping missing file: {local_path}")
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continue
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print(f"Uploading file {local_path} -> {remote_path}")
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api.upload_file(
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path_or_fileobj=str(local_path),
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path_in_repo=remote_path,
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repo_id=repo_id,
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repo_type=repo_type,
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)
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for local_path, remote_path in REQUIRED_ASSET_FILES.items():
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if not local_path.exists():
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print(f"Skipping missing asset: {local_path}")
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continue
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print(f"Uploading asset {local_path} -> {remote_path}")
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api.upload_file(
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path_or_fileobj=str(local_path),
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path_in_repo=remote_path,
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repo_id=repo_id,
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repo_type=repo_type,
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)
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def upload_folders(api: HfApi, repo_id: str, repo_type: str):
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for local_path, remote_path in REQUIRED_ASSET_FOLDERS.items():
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if not local_path.exists():
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print(f"Skipping missing folder: {local_path}")
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continue
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print(f"Uploading folder {local_path} -> {remote_path}")
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api.upload_folder(
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folder_path=str(local_path),
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path_in_repo=remote_path,
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repo_id=repo_id,
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repo_type=repo_type,
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)
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def main():
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parser = argparse.ArgumentParser(
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description="Upload the minimal used SynLayers demo bundle to a Hugging Face repo."
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)
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parser.add_argument("--repo-id", type=str, default="SynLayers/Bbox-caption-8b")
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parser.add_argument("--repo-type", type=str, default="model", choices=["model", "space", "dataset"])
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parser.add_argument("--token", type=str, default=os.environ.get("HF_TOKEN"))
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args = parser.parse_args()
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if not args.token:
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raise ValueError("Missing Hugging Face token. Pass --token or set HF_TOKEN.")
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api = HfApi(token=args.token)
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upload_files(api, args.repo_id, args.repo_type)
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upload_folders(api, args.repo_id, args.repo_type)
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print("")
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print(f"Finished uploading used demo bundle to https://huggingface.co/{args.repo_id}")
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if __name__ == "__main__":
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main()
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