Instructions to use bdsqlsz/Qwen3-VL-4B-Reweight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bdsqlsz/Qwen3-VL-4B-Reweight with Transformers:

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

pipe = pipeline("image-text-to-text", model="bdsqlsz/Qwen3-VL-4B-Reweight")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bdsqlsz/Qwen3-VL-4B-Reweight", dtype="auto")

Diffusers

How to use bdsqlsz/Qwen3-VL-4B-Reweight with Diffusers:

pip install -U diffusers transformers accelerate

import torch
from diffusers import DiffusionPipeline

# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("bdsqlsz/Qwen3-VL-4B-Reweight", dtype=torch.bfloat16, device_map="cuda")

prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bdsqlsz/Qwen3-VL-4B-Reweight with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bdsqlsz/Qwen3-VL-4B-Reweight"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bdsqlsz/Qwen3-VL-4B-Reweight",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bdsqlsz/Qwen3-VL-4B-Reweight

SGLang

How to use bdsqlsz/Qwen3-VL-4B-Reweight 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 "bdsqlsz/Qwen3-VL-4B-Reweight" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bdsqlsz/Qwen3-VL-4B-Reweight",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "bdsqlsz/Qwen3-VL-4B-Reweight" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bdsqlsz/Qwen3-VL-4B-Reweight",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bdsqlsz/Qwen3-VL-4B-Reweight with Docker Model Runner:
```
docker model run hf.co/bdsqlsz/Qwen3-VL-4B-Reweight
```

Citation

If you find our work helpful, feel free to give us a cite.

@article{jiang2026dopsd,
      title={D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models},
      author={Jiang, Dengyang and Jin, Xin and Liu, Dongyang and Wang, Zanyi and Zheng, Mingzhe and Du, Ruoyi and Yang, Xiangpeng and Wu, Qilong and Li, Zhen and Gao, Peng and Yang, Harry and Hoi, Steven},
      journal={arXiv preprint arXiv:2605.05204},
      year={2026}
}

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Paper for bdsqlsz/Qwen3-VL-4B-Reweight

D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models

Paper • 2605.05204 • Published May 6 • 29