Instructions to use MING-ZCH/MetaphorStar-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MING-ZCH/MetaphorStar-3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="MING-ZCH/MetaphorStar-3B")
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("MING-ZCH/MetaphorStar-3B")
model = AutoModelForImageTextToText.from_pretrained("MING-ZCH/MetaphorStar-3B")
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 MING-ZCH/MetaphorStar-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MING-ZCH/MetaphorStar-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MING-ZCH/MetaphorStar-3B",
		"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/MING-ZCH/MetaphorStar-3B

SGLang

How to use MING-ZCH/MetaphorStar-3B 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 "MING-ZCH/MetaphorStar-3B" \
    --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": "MING-ZCH/MetaphorStar-3B",
		"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 "MING-ZCH/MetaphorStar-3B" \
        --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": "MING-ZCH/MetaphorStar-3B",
		"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 MING-ZCH/MetaphorStar-3B with Docker Model Runner:
```
docker model run hf.co/MING-ZCH/MetaphorStar-3B
```

Add pipeline tag and external links

by nielsr HF Staff - opened Feb 12

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+10

-4

Files changed (1) hide show

README.md +10 -4

README.md CHANGED Viewed

@@ -1,17 +1,21 @@
 ---
-license: apache-2.0
 library_name: transformers
 tags:
 - vision-language-model
 - reinforcement-learning
 - grpo
 - metaphor-understanding
 - visual-reasoning
-base_model: Qwen/Qwen2.5-VL
 ---
 # MetaphorStar: Image Metaphor Understanding and Reasoning with End-to-End Visual RL
 **MetaphorStar** is the first Multi-modal Large Language Model (MLLM) family trained via an **End-to-End Visual Reinforcement Learning (RL)** framework specifically designed to bridge the gap between literal perception ("seeing things as they are") and metaphorical understanding ("seeing things as we are").
 Built upon the Qwen2.5-VL architecture, MetaphorStar achieves State-of-the-Art (SOTA) performance on image implication tasks and demonstrates robust generalization capabilities on complex visual reasoning benchmarks (e.g., MMMU, MathVerse).
@@ -65,7 +69,9 @@ messages = [
         "role": "user",
         "content": [
             {"type": "image", "image": "path/to/metaphor_image.jpg"},
-            {"type": "text", "text": "True-false questions: The wilted plant in the office implies a stressful working environment.\n\nFirst, describe the image, then analyze the image implication, and finally reason to get the answer. Output the thinking process in <think></think> and the final correct answer in <answer></answer> tags."}
         ]
     }
 ]
@@ -85,7 +91,7 @@ print(output_text)
 @article{metaphorstar2026,
   title={MetaphorStar: Image Metaphor Understanding and Reasoning with End-to-End Visual Reinforcement Learning},
   author={Chenhao Zhang, Yazhe Niu, Hongsheng Li},
-  journal={Anonymous},
   year={2026}
 }
 ```

 ---
+base_model: Qwen/Qwen2.5-VL
 library_name: transformers
+license: apache-2.0
+pipeline_tag: image-text-to-text
+arxiv: 2602.10575
 tags:
 - vision-language-model
 - reinforcement-learning
 - grpo
 - metaphor-understanding
 - visual-reasoning
 ---
 # MetaphorStar: Image Metaphor Understanding and Reasoning with End-to-End Visual RL
+[**Paper**](https://huggingface.co/papers/2602.10575) | [**Project Page**](https://metaphorstar.github.io) | [**GitHub**](https://github.com/MING-ZCH/MetaphorStar)
 **MetaphorStar** is the first Multi-modal Large Language Model (MLLM) family trained via an **End-to-End Visual Reinforcement Learning (RL)** framework specifically designed to bridge the gap between literal perception ("seeing things as they are") and metaphorical understanding ("seeing things as we are").
 Built upon the Qwen2.5-VL architecture, MetaphorStar achieves State-of-the-Art (SOTA) performance on image implication tasks and demonstrates robust generalization capabilities on complex visual reasoning benchmarks (e.g., MMMU, MathVerse).
         "role": "user",
         "content": [
             {"type": "image", "image": "path/to/metaphor_image.jpg"},
+            {"type": "text", "text": "True-false questions: The wilted plant in the office implies a stressful working environment.
+First, describe the image, then analyze the image implication, and finally reason to get the answer. Output the thinking process in <think></think> and the final correct answer in <answer></answer> tags."}
         ]
     }
 ]
 @article{metaphorstar2026,
   title={MetaphorStar: Image Metaphor Understanding and Reasoning with End-to-End Visual Reinforcement Learning},
   author={Chenhao Zhang, Yazhe Niu, Hongsheng Li},
+  journal={arXiv preprint arXiv:2602.10575},
   year={2026}
 }
 ```