Instructions to use TengfeiLiuCoder/RefCaptioner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TengfeiLiuCoder/RefCaptioner with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="TengfeiLiuCoder/RefCaptioner")
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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("TengfeiLiuCoder/RefCaptioner")
model = AutoModelForMultimodalLM.from_pretrained("TengfeiLiuCoder/RefCaptioner", device_map="auto")
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 Settings

vLLM

How to use TengfeiLiuCoder/RefCaptioner with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TengfeiLiuCoder/RefCaptioner"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TengfeiLiuCoder/RefCaptioner",
		"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/TengfeiLiuCoder/RefCaptioner

SGLang

How to use TengfeiLiuCoder/RefCaptioner 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 "TengfeiLiuCoder/RefCaptioner" \
    --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": "TengfeiLiuCoder/RefCaptioner",
		"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 "TengfeiLiuCoder/RefCaptioner" \
        --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": "TengfeiLiuCoder/RefCaptioner",
		"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 TengfeiLiuCoder/RefCaptioner with Docker Model Runner:
```
docker model run hf.co/TengfeiLiuCoder/RefCaptioner
```

TengfeiLiuCoder commited on 6 days ago

Commit

403ebeb

verified ·

1 Parent(s): 39b517d

Update inference environment documentation

Browse files

Files changed (3) hide show

README.md +4 -2
requirement.txt +12 -0
requirements.txt +0 -5

README.md CHANGED Viewed

@@ -33,8 +33,10 @@ The released weights are a merged Hugging Face checkpoint based on [Qwen3-VL-8B-
 ## Installation
 ```bash
-pip install -r requirements.txt
 ```
 RefCaptioner uses the standard Qwen3-VL inference stack and does not require a task-specific runtime. Any environment that can run Qwen3-VL with a compatible Transformers release can be used directly.
@@ -45,7 +47,7 @@ The benchmark environment used `torch==2.9.0`, `transformers==4.57.6`, `accelera
 ```bash
 python inference.py \
-  --model NOVAglow646/RefCaptioner \
   --video /path/to/video.mp4 \
   --images /path/to/ref_1.jpg /path/to/ref_2.jpg /path/to/ref_3.png
 ```

 ## Installation
+The tested environment for loading RefCaptioner and running inference is provided in `requirement.txt`. Use Python 3.10 and install it with:
 ```bash
+pip install -r requirement.txt
 ```
 RefCaptioner uses the standard Qwen3-VL inference stack and does not require a task-specific runtime. Any environment that can run Qwen3-VL with a compatible Transformers release can be used directly.
 ```bash
 python inference.py \
+  --model TengfeiLiuCoder/RefCaptioner \
   --video /path/to/video.mp4 \
   --images /path/to/ref_1.jpg /path/to/ref_2.jpg /path/to/ref_3.png
 ```

requirement.txt ADDED Viewed

	@@ -0,0 +1,12 @@

+# Tested model inference environment for RefCaptioner (Python 3.10).
+torch==2.9.0
+torchvision==0.24.0
+transformers==4.57.6
+accelerate==1.14.0
+qwen-vl-utils==0.0.14
+av==17.1.0
+pillow==12.2.0
+numpy==2.2.6
+peft==0.19.1
+safetensors==0.8.0
+einops==0.8.2

requirements.txt DELETED Viewed

@@ -1,5 +0,0 @@
-torch>=2.4
-transformers>=4.57.0,<5
-accelerate>=1.0
-qwen-vl-utils>=0.0.14
-av>=14