Instructions to use SynLayers/Bbox-caption-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Bbox-caption-8b

File size: 3,772 Bytes

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# Main real-world inference configuration
seed: 42
max_layer_num: 52

# Size configuration
source_size: 1024
target_size: 1024

# Real-world inference defaults
data_dir: "/project/llmsvgen/share/data/kmw_layered_dataset/real_world_inference"
image_dir: "/project/llmsvgen/share/data/kmw_layered_dataset/real_world_inference/layers_real_test_1024"
test_jsonl: "/project/llmsvgen/share/data/kmw_layered_dataset/real_world_inference/caption_bbox_infer.jsonl"

# Model paths
pretrained_model_name_or_path: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_checkpoints/FLUX.1-dev"
pretrained_adapter_path: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_checkpoints/FLUX.1-dev-Controlnet-Inpainting-Alpha"
transp_vae_path: "ckpt/trans_vae/0008000.pt"

# Pre-trained LoRA weights
pretrained_lora_dir: "ckpt/pre_trained_LoRA"
artplus_lora_dir: "ckpt/prism_ft_LoRA"

# below is for 18k dataset
#lora_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_18k/step_90000/transformer"
#layer_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_18k/step_90000"
#adapter_lora_dir: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_18k/step_90000/adapter"

# below is for 20k dataset
#lora_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_20k/step_120000/transformer"
#layer_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_20k/step_120000"
#adapter_lora_dir: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_20k/step_120000/adapter"

# below is for 30k dataset
#lora_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_30k/step_150000/transformer"
#layer_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_30k/step_150000"
#adapter_lora_dir: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_30k/step_150000/adapter"

# below is for 40k dataset
#lora_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_40k/step_250000/transformer"
#layer_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_40k/step_250000"
#adapter_lora_dir: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_40k/step_250000/adapter"

# below is for 50k dataset
#lora_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_50k/step_200000/transformer"
#layer_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_50k/step_200000"
#adapter_lora_dir: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_train_dataset/ckpt_prism_scaleup_1024_50k/step_200000/adapter"

# unified real-world decomposition checkpoint
lora_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_ckpt/step_120000/transformer"
layer_ckpt: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_ckpt/step_120000"
adapter_lora_dir: "/project/llmsvgen/share/data/kmw_layered_checkpoint/SynLayers_ckpt/step_120000/adapter"

# Inference settings
cfg: 4.0
adapter_scale: 0.9
max_sequence_length: 1024

save_dir: "/project/llmsvgen/share/data/kmw_layered_dataset/real_world_inference/results"
#run_name: "step_120000"  # optional manual override

# Sample range control (1-based indexing)
start_idx: 1
#end_idx: 147
#max_samples: 147