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: 2,753 Bytes
06be361 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 | #!/usr/bin/env bash
set -euo pipefail
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
PYTHON_BIN="${PYTHON_BIN:-python}"
: "${BLENDED_DIR:?Set BLENDED_DIR}"
: "${LAION_DIR:?Set LAION_DIR}"
: "${CAPTION_DIR:?Set CAPTION_DIR}"
: "${OUTPUT_DIR:?Set OUTPUT_DIR}"
CAPTION_META="${CAPTION_META:-${CAPTION_DIR}/captions.jsonl}"
NUM_SAMPLES="${NUM_SAMPLES:-500000}"
START_INDEX="${START_INDEX:-0}"
SEED="${SEED:-42}"
MAX_BASE_SAMPLES="${MAX_BASE_SAMPLES:-18000}"
MIN_DONOR_SAMPLES="${MIN_DONOR_SAMPLES:-1}"
MAX_DONOR_SAMPLES="${MAX_DONOR_SAMPLES:-4}"
MIN_LAYERS_PER_DONOR="${MIN_LAYERS_PER_DONOR:-0}"
MAX_LAYERS_PER_DONOR="${MAX_LAYERS_PER_DONOR:-2}"
MIN_LAYERS_TO_REMOVE="${MIN_LAYERS_TO_REMOVE:-1}"
MAX_LAYERS_TO_REMOVE="${MAX_LAYERS_TO_REMOVE:-4}"
ADDED_LAYER_MIN_SIZE="${ADDED_LAYER_MIN_SIZE:-0.9}"
ADDED_LAYER_MAX_SIZE="${ADDED_LAYER_MAX_SIZE:-1.1}"
LAION_PROB="${LAION_PROB:-0.60}"
CAPTION_PROB="${CAPTION_PROB:-0.35}"
LAION_MIN_SIZE="${LAION_MIN_SIZE:-0.3}"
LAION_MAX_SIZE="${LAION_MAX_SIZE:-0.4}"
CAPTION_MIN_SIZE="${CAPTION_MIN_SIZE:-0.6}"
CAPTION_MAX_SIZE="${CAPTION_MAX_SIZE:-0.8}"
ALPHAVAE_MIN_LAYERS="${ALPHAVAE_MIN_LAYERS:-0}"
ALPHAVAE_MAX_LAYERS="${ALPHAVAE_MAX_LAYERS:-0}"
ALPHAVAE_MIN_SIZE="${ALPHAVAE_MIN_SIZE:-0.25}"
ALPHAVAE_MAX_SIZE="${ALPHAVAE_MAX_SIZE:-0.40}"
NUM_WORKERS="${NUM_WORKERS:-64}"
SKIP_EXISTING="${SKIP_EXISTING:-0}"
cmd=(
"$PYTHON_BIN" "$SCRIPT_DIR/scaleup_dataset.py"
--blended_dir "$BLENDED_DIR"
--laion_dir "$LAION_DIR"
--caption_dir "$CAPTION_DIR"
--caption_meta "$CAPTION_META"
--output_dir "$OUTPUT_DIR"
--num_samples "$NUM_SAMPLES"
--start_index "$START_INDEX"
--seed "$SEED"
--min_donor_samples "$MIN_DONOR_SAMPLES"
--max_donor_samples "$MAX_DONOR_SAMPLES"
--min_layers_per_donor "$MIN_LAYERS_PER_DONOR"
--max_layers_per_donor "$MAX_LAYERS_PER_DONOR"
--added_layer_min_size "$ADDED_LAYER_MIN_SIZE"
--added_layer_max_size "$ADDED_LAYER_MAX_SIZE"
--laion_prob "$LAION_PROB"
--caption_prob "$CAPTION_PROB"
--laion_min_size "$LAION_MIN_SIZE"
--laion_max_size "$LAION_MAX_SIZE"
--caption_min_size "$CAPTION_MIN_SIZE"
--caption_max_size "$CAPTION_MAX_SIZE"
--min_layers_to_remove "$MIN_LAYERS_TO_REMOVE"
--max_layers_to_remove "$MAX_LAYERS_TO_REMOVE"
--alphavae_min_layers "$ALPHAVAE_MIN_LAYERS"
--alphavae_max_layers "$ALPHAVAE_MAX_LAYERS"
--alphavae_min_size "$ALPHAVAE_MIN_SIZE"
--alphavae_max_size "$ALPHAVAE_MAX_SIZE"
--max_base_samples "$MAX_BASE_SAMPLES"
--num_workers "$NUM_WORKERS"
)
if [[ -n "${ALPHAVAE_DIR:-}" ]]; then
cmd+=(--alphavae_dir "$ALPHAVAE_DIR")
fi
if [[ -n "${ALPHAVAE_PROMPTS:-}" ]]; then
cmd+=(--alphavae_prompts "$ALPHAVAE_PROMPTS")
fi
if [[ "$SKIP_EXISTING" == "1" ]]; then
cmd+=(--skip_existing)
fi
"${cmd[@]}"
|