Instructions to use AirRain03/qwen2vl_32b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AirRain03/qwen2vl_32b with Transformers:

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

pipe = pipeline("image-text-to-text", model="AirRain03/qwen2vl_32b")
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("AirRain03/qwen2vl_32b")
model = AutoModelForMultimodalLM.from_pretrained("AirRain03/qwen2vl_32b", 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 AirRain03/qwen2vl_32b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AirRain03/qwen2vl_32b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AirRain03/qwen2vl_32b",
		"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/AirRain03/qwen2vl_32b

SGLang

How to use AirRain03/qwen2vl_32b 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 "AirRain03/qwen2vl_32b" \
    --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": "AirRain03/qwen2vl_32b",
		"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 "AirRain03/qwen2vl_32b" \
        --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": "AirRain03/qwen2vl_32b",
		"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 AirRain03/qwen2vl_32b with Docker Model Runner:
```
docker model run hf.co/AirRain03/qwen2vl_32b
```

qwen2vl_32b

File size: 1,399 Bytes

---
base_model: Qwen/Qwen2.5-VL-32B-Instruct
library_name: transformers
pipeline_tag: image-text-to-text
license: mit
---

# qwen2vl_32b

This is a PE-finetuned Qwen2.5-VL prompt-enrichment checkpoint for PIPBench, presented in the paper [PIPBench: A Profile-Inclusive Framework for Personalized Image Generation Evaluation](https://huggingface.co/papers/2607.06440).

It takes PIPBench reference images plus the original text prompt and outputs an enriched prompt. Generate final images by running Qwen-Image with the enriched prompt.

These weights come from the PIPBench codex/ablations/PE experiments and are not QIP checkpoints.

## Resources

- **Paper:** [PIPBench: A Profile-Inclusive Framework for Personalized Image Generation Evaluation](https://huggingface.co/papers/2607.06440)
- **Project Page:** [https://wuyuhang05.github.io/PIPBench/](https://wuyuhang05.github.io/PIPBench/)
- **Repository:** [https://github.com/wuyuhang05/PIPBench](https://github.com/wuyuhang05/PIPBench)

## Usage

To generate enriched prompts using this 32B PE module, clone the [official repository](https://github.com/wuyuhang05/PIPBench) and run:

```bash
bash scripts/enrich_prompts_qwen2vl.sh \
  --model-id AirRain03/qwen2vl_32b \
  --metadata data/pipbench/metadata.json \
  --data-root data/pipbench \
  --out outputs/prompts/qwen2vl_32b.jsonl \
  --device-map auto \
  --dtype bfloat16 \
  --resume
```