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
```

SynLayers commited on 7 days ago

Commit

b6a8276

verified ·

1 Parent(s): e984584

Upload demo/hf_repo_assets.py with huggingface_hub

Browse files

Files changed (1) hide show

demo/hf_repo_assets.py +16 -5

demo/hf_repo_assets.py CHANGED Viewed

@@ -15,6 +15,14 @@ def get_cache_dir() -> str | None:
     return os.environ.get("SYNLAYERS_HF_CACHE")
 @lru_cache(maxsize=4)
 def ensure_repo_assets(repo_id: str | None = None) -> Path | None:
     """Download required runtime assets from the uploaded model repo when configured."""
@@ -23,13 +31,14 @@ def ensure_repo_assets(repo_id: str | None = None) -> Path | None:
         return None
     allow_patterns = [
-        "SynLayers_checkpoints/FLUX.1-dev/**",
         "SynLayers_checkpoints/FLUX.1-dev-Controlnet-Inpainting-Alpha/**",
         "SynLayers_ckpt/step_120000/**",
         "ckpt/trans_vae/0008000.pt",
         "ckpt/pre_trained_LoRA/**",
         "ckpt/prism_ft_LoRA/**",
     ]
     local_root = snapshot_download(
         repo_id=resolved_repo_id,
@@ -46,12 +55,9 @@ def build_repo_asset_overrides(repo_id: str | None = None) -> dict[str, str]:
     if local_root is None:
         return {}
-    return {
         "repo_root": str(local_root),
         "decomp_ckpt_root": str(local_root / "SynLayers_ckpt" / "step_120000"),
-        "pretrained_model_name_or_path": str(
-            local_root / "SynLayers_checkpoints" / "FLUX.1-dev"
-        ),
         "pretrained_adapter_path": str(
             local_root
             / "SynLayers_checkpoints"
@@ -61,3 +67,8 @@ def build_repo_asset_overrides(repo_id: str | None = None) -> dict[str, str]:
         "pretrained_lora_dir": str(local_root / "ckpt" / "pre_trained_LoRA"),
         "artplus_lora_dir": str(local_root / "ckpt" / "prism_ft_LoRA"),
     }

     return os.environ.get("SYNLAYERS_HF_CACHE")
+def should_download_repo_flux() -> bool:
+    """Download FLUX from the model repo only when no external base model is configured."""
+    base_model = os.environ.get("SYNLAYERS_BASE_MODEL", "").strip()
+    if not base_model:
+        return True
+    return base_model.startswith(get_model_repo_id() or "")
 @lru_cache(maxsize=4)
 def ensure_repo_assets(repo_id: str | None = None) -> Path | None:
     """Download required runtime assets from the uploaded model repo when configured."""
         return None
     allow_patterns = [
         "SynLayers_checkpoints/FLUX.1-dev-Controlnet-Inpainting-Alpha/**",
         "SynLayers_ckpt/step_120000/**",
         "ckpt/trans_vae/0008000.pt",
         "ckpt/pre_trained_LoRA/**",
         "ckpt/prism_ft_LoRA/**",
     ]
+    if should_download_repo_flux():
+        allow_patterns.insert(0, "SynLayers_checkpoints/FLUX.1-dev/**")
     local_root = snapshot_download(
         repo_id=resolved_repo_id,
     if local_root is None:
         return {}
+    overrides = {
         "repo_root": str(local_root),
         "decomp_ckpt_root": str(local_root / "SynLayers_ckpt" / "step_120000"),
         "pretrained_adapter_path": str(
             local_root
             / "SynLayers_checkpoints"
         "pretrained_lora_dir": str(local_root / "ckpt" / "pre_trained_LoRA"),
         "artplus_lora_dir": str(local_root / "ckpt" / "prism_ft_LoRA"),
     }
+    if should_download_repo_flux():
+        overrides["pretrained_model_name_or_path"] = str(
+            local_root / "SynLayers_checkpoints" / "FLUX.1-dev"
+        )
+    return overrides