Instructions to use YeMoKoo/SDVC_perception_VL-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YeMoKoo/SDVC_perception_VL-2B with Transformers:

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

pipe = pipeline("image-text-to-text", model="YeMoKoo/SDVC_perception_VL-2B")
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("YeMoKoo/SDVC_perception_VL-2B")
model = AutoModelForMultimodalLM.from_pretrained("YeMoKoo/SDVC_perception_VL-2B")
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 YeMoKoo/SDVC_perception_VL-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YeMoKoo/SDVC_perception_VL-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YeMoKoo/SDVC_perception_VL-2B",
		"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/YeMoKoo/SDVC_perception_VL-2B

SGLang

How to use YeMoKoo/SDVC_perception_VL-2B 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 "YeMoKoo/SDVC_perception_VL-2B" \
    --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": "YeMoKoo/SDVC_perception_VL-2B",
		"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 "YeMoKoo/SDVC_perception_VL-2B" \
        --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": "YeMoKoo/SDVC_perception_VL-2B",
		"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 YeMoKoo/SDVC_perception_VL-2B with Docker Model Runner:
```
docker model run hf.co/YeMoKoo/SDVC_perception_VL-2B
```

SDVC_perception_VL-2B / README.md

YeMoKoo

Add SDVC perception VL model package

9351001 verified 2 months ago

preview code

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	---
	license: apache-2.0
	pipeline_tag: image-text-to-text
	library_name: transformers
	base_model: Qwen/Qwen3-VL-8B-Instruct
	tags:
	- vision-language
	- image-text-to-text
	- qwen3-vl
	- sdvc
	---

	# SDVC_perception_VL-2B

	This repository packages a vision-language checkpoint for SDVC perception experiments.

	The checkpoint files and architecture configuration are intentionally kept compatible with the original Qwen3-VL release. Only the repository-facing model card and loading examples have been changed for this packaging task.

	## Usage

	```python
	from transformers import AutoProcessor, Qwen3VLForConditionalGeneration

	repo_id = "YeMoKoo/SDVC_preception_VL-2B"

	model = Qwen3VLForConditionalGeneration.from_pretrained(
	repo_id,
	dtype="auto",
	device_map="auto",
	)
	processor = AutoProcessor.from_pretrained(repo_id)
	```

	## Example

	```python
	messages = [
	{
	"role": "user",
	"content": [
	{
	"type": "image",
	"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
	},
	{"type": "text", "text": "Describe this image."},
	],
	}
	]

	inputs = processor.apply_chat_template(
	messages,
	tokenize=True,
	add_generation_prompt=True,
	return_dict=True,
	return_tensors="pt",
	).to(model.device)

	generated_ids = model.generate(**inputs, max_new_tokens=128)
	generated_ids_trimmed = [
	out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
	]
	output_text = processor.batch_decode(
	generated_ids_trimmed,
	skip_special_tokens=True,
	clean_up_tokenization_spaces=False,
	)
	print(output_text)
	```

	## Notes

	- Target Hub repository: `YeMoKoo/SDVC_preception_VL-2B`
	- Display name used in this card: `SDVC_perception_VL-2B`
	- The safetensors checkpoint files are unchanged from the local source checkpoint.
	- Structural config values are unchanged so the model remains load-compatible with Transformers.

	## Citation

	```bibtex
	@misc{qwen3technicalreport,
	title={Qwen3 Technical Report},
	author={Qwen Team},
	year={2025},
	eprint={2505.09388},
	archivePrefix={arXiv},
	primaryClass={cs.CL},
	url={https://arxiv.org/abs/2505.09388},
	}
	```