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

Libraries

How to use yuandaxia/ProCIR with Transformers:

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

pipe = pipeline("image-text-to-text", model="yuandaxia/ProCIR")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("yuandaxia/ProCIR")
model = AutoModelForImageTextToText.from_pretrained("yuandaxia/ProCIR")
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 yuandaxia/ProCIR with vLLM:

Install from pip and serve model

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

SGLang

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

Add metadata and update paper links

by nielsr HF Staff - opened Apr 14

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+11

-2

Files changed (1) hide show

README.md +11 -2

README.md CHANGED Viewed

@@ -1,6 +1,15 @@
 # ProCIR — Multi-View Product-Level Composed Image Retrieval
-[[Paper (arXiv)]](https://arxiv.org/abs/xxxx.xxxxx) | [[Code (GitHub)]](https://github.com/yuandaxia2001/FashionMV) | [[Dataset]](https://huggingface.co/datasets/yuandaxia/FashionMV)
 ## Model Description
@@ -38,4 +47,4 @@ model = Qwen3_5ForConditionalGeneration.from_pretrained("yuandaxia/ProCIR", torc
 ## License
-Model weights are released under the same license as the base model ([Qwen3.5](https://huggingface.co/Qwen/Qwen3.5-0.8B)).

+---
+license: other
+library_name: transformers
+pipeline_tag: image-text-to-text
+base_model: Qwen/Qwen3.5-0.8B
+datasets:
+- yuandaxia/FashionMV
+---
 # ProCIR — Multi-View Product-Level Composed Image Retrieval
+[[Paper (arXiv)]](https://arxiv.org/abs/2604.10297) | [[Code (GitHub)]](https://github.com/yuandaxia2001/FashionMV) | [[Dataset]](https://huggingface.co/datasets/yuandaxia/FashionMV)
 ## Model Description
 ## License
+Model weights are released under the same license as the base model ([Qwen3.5](https://huggingface.co/Qwen/Qwen3.5-0.8B)).