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
```

nielsr HF Staff commited on Jul 8

Commit

1f09ad0

verified ·

1 Parent(s): 1b1b2bc

Add paper link, project page, repository, and usage instructions

Browse files

This PR updates the model card for the `qwen2vl_32b` prompt-enrichment checkpoint from PIPBench. It links the checkpoint to the official paper, project page, and GitHub repository, specifies the MIT license in the metadata, and adds usage instructions directly from the official repository.

Files changed (1) hide show

README.md +27 -7

README.md CHANGED Viewed

@@ -1,15 +1,35 @@
 ---
 library_name: transformers
 pipeline_tag: image-text-to-text
-base_model: Qwen/Qwen2.5-VL-32B-Instruct
 ---
 # qwen2vl_32b
-This is a PE-finetuned Qwen2.5-VL prompt-enrichment checkpoint for PIPBench.
-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.

 ---
+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
+```