Instructions to use mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-nothink 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-nothink 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-nothink")
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-nothink")
model = AutoModelForImageTextToText.from_pretrained("mengrui6351/Qwen3-VL-2B-GRPO-MRI-600-nothink")
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-nothink 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-nothink"
# 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-nothink",
		"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-nothink

SGLang

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

Qwen3-VL-2B-GRPO-MRI-600-nothink / README.md

mengrui6351

Qwen3 VL 2B Instruct GRPOed without thinking by MRI 600 data.

7493924 verified 5 months ago

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	---
	base_model: Qwen/Qwen3-VL-2B-Instruct
	library_name: transformers
	model_name: Qwen3-VL-2B-GRPO-MRI-600-nothink
	tags:
	- generated_from_trainer
	- gold_multimodal
	- trl
	licence: license
	---

	# Model Card for Qwen3-VL-2B-GRPO-MRI-600-nothink

	This model is a fine-tuned version of [Qwen/Qwen3-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct).
	It has been trained using [TRL](https://github.com/huggingface/trl).

	## Quick start

	```python
	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

	[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/mengrui6351/huggingface/runs/seytsgde)


	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:

	```bibtex
	@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:

	```bibtex
	@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}}
	}
	```