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
```

ProCIR — Multi-View Product-Level Composed Image Retrieval

[Paper (arXiv)] | [Code (GitHub)] | [Dataset]

Model Description

ProCIR (0.8B) is a multi-view composed image retrieval model trained on the FashionMV dataset, based on Qwen3.5-0.8B. It adopts a perception-reasoning decoupled dialogue architecture and leverages image-text alignment to inject product knowledge, enabling effective multi-view product-level CIR.

Performance

Dataset	R@5	R@10
DeepFashion	89.2	94.9
Fashion200K	77.6	86.6
FashionGen-val	75.0	85.3
Average	80.6	88.9

Usage

See our GitHub repository for evaluation code and data preparation instructions.

from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

processor = AutoProcessor.from_pretrained("yuandaxia/ProCIR")
model = Qwen3_5ForConditionalGeneration.from_pretrained("yuandaxia/ProCIR", torch_dtype="bfloat16")

Citation

@article{yuan2026fashionmv,
  title={FashionMV: Product-Level Composed Image Retrieval with Multi-View Fashion Data},
  author={Yuan, Peng and Mei, Bingyin and Zhang, Hui},
  year={2026}
}

License

Model weights are released under the same license as the base model (Qwen3.5).

Downloads last month: 7

Safetensors

Model size

0.9B params

Tensor type

BF16

Model tree for yuandaxia/ProCIR

Base model

Qwen/Qwen3.5-0.8B-Base

Finetuned

Qwen/Qwen3.5-0.8B

Finetuned

(221)

this model

Dataset used to train yuandaxia/ProCIR

Paper for yuandaxia/ProCIR

FashionMV: Product-Level Composed Image Retrieval with Multi-View Fashion Data

Paper • 2604.10297 • Published Apr 11