Instructions to use mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-think with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-think with Transformers:

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

pipe = pipeline("image-text-to-text", model="mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-think")
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("mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-think")
model = AutoModelForImageTextToText.from_pretrained("mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-think")
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

vLLM

How to use mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-think with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-think"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-think",
		"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/mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-think

SGLang

How to use mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-think 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 "mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-think" \
    --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": "mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-think",
		"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 "mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-think" \
        --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": "mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-think",
		"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 mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-think with Docker Model Runner:
```
docker model run hf.co/mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-think
```

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Model Card for Qwen3-VL-2B-GRPO-MRI-600-think

This model is a fine-tuned version of Qwen/Qwen3-VL-2B-Instruct. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with GOLDMultimodal.

Framework versions

TRL: 0.26.2
Transformers: 4.57.3
Pytorch: 2.8.0+cu128
Datasets: 4.4.2
Tokenizers: 0.22.1

Citations

Cite GOLDMultimodal as:

@misc{patino2025unlocking,
    title        = {{Unlocking On-Policy Distillation for Any Model Family}},
    author       = {Carlos Miguel Patiño and Kashif Rasul and Quentin Gallouédec and Ben Burtenshaw and Sergio Paniego and Vaibhav Srivastav and Thibaud Frere and Ed Beeching and Lewis Tunstall and Leandro von Werra and Thomas Wolf},
    year         = 2025,
    url          = {https://huggingface.co/spaces/HuggingFaceH4/general-on-policy-logit-distillation},
}

Cite TRL as:

@misc{vonwerra2022trl,
    title        = {{TRL: Transformer Reinforcement Learning}},
    author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
    year         = 2020,
    journal      = {GitHub repository},
    publisher    = {GitHub},
    howpublished = {\url{https://github.com/huggingface/trl}}
}

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-think

Base model

Qwen/Qwen3-VL-2B-Instruct

Finetuned

(212)

this model