Instructions to use internlm/Spatial-SSRL-Qwen3VL-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use internlm/Spatial-SSRL-Qwen3VL-4B with Transformers:

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

pipe = pipeline("image-text-to-text", model="internlm/Spatial-SSRL-Qwen3VL-4B")
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("internlm/Spatial-SSRL-Qwen3VL-4B")
model = AutoModelForImageTextToText.from_pretrained("internlm/Spatial-SSRL-Qwen3VL-4B")
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 internlm/Spatial-SSRL-Qwen3VL-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/Spatial-SSRL-Qwen3VL-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/Spatial-SSRL-Qwen3VL-4B",
		"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/internlm/Spatial-SSRL-Qwen3VL-4B

SGLang

How to use internlm/Spatial-SSRL-Qwen3VL-4B 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 "internlm/Spatial-SSRL-Qwen3VL-4B" \
    --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": "internlm/Spatial-SSRL-Qwen3VL-4B",
		"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 "internlm/Spatial-SSRL-Qwen3VL-4B" \
        --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": "internlm/Spatial-SSRL-Qwen3VL-4B",
		"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 internlm/Spatial-SSRL-Qwen3VL-4B with Docker Model Runner:
```
docker model run hf.co/internlm/Spatial-SSRL-Qwen3VL-4B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Spatial-SSRL-Qwen3VL-4B

Spatial-SSRL-Qwen3VL-4B is a large vision-language model targeting spatial understanding, built on the base of Qwen3-VL-4B-Instruct. It's optimized by applying Spatial-SSRL, a lightweight self-supervised reinforcement learning paradigm which can scale RLVR efficiently. The model demonstrates strong spatial intelligence while preserving the original general visual capabilities of the base model.

📢 News

🚀 [2026/04/05] We have released the training code of Spatial-SSRL.
🚀 [2026/02/25] We have released the 🤗Spatial-SSRL-3B Model, initialized from Qwen2.5-VL-3B-Instruct.
🚀 [2026/02/21] Our work has been accepted by CVPR 2026.
🚀 [2025/11/24] We have released the 🤗Spatial-SSRL-Qwen3VL-4B Model, initialized from Qwen3-VL-4B-Instruct.
🚀 [2025/11/03] Now you can try out Spatial-SSRL-7B on 🤗Spatial-SSRL Space.
🚀 [2025/11/03] We have released the 🤗Spatial-SSRL-7B Model, and 🤗Spatial-SSRL-81k Dataset.
🚀 [2025/11/02] We have released the 🏠Spatial-SSRL Repository.

🌈 Overview

We are thrilled to introduce Spatial-SSRL, a novel self-supervised RL paradigm aimed at enhancing LVLM spatial understanding. By optimizing Qwen2.5-VL-7B with Spatial-SSRL, the model exhibits stronger spatial intelligence across seven spatial understanding benchmarks in both image and video settings.

Teaser

Spatial-SSRL is a lightweight tool-free framework that is natually compatible with the RLVR training paradigm and easy to extend to a multitude of pretext tasks. Five tasks are currently formulated in the framework, requiring only ordinary RGB and RGB-D images. And we welcome you to join Spatial-SSRL with effective pretext tasks to further strengthen the capabilities of LVLMs!

Pipeline

💡 Highlights

🔥 Highly Scalable: Spatial-SSRL uses ordinary raw RGB and RGB-D images instead of richly-annotated public datasets or manual labels for data curation, making it highly scalable.
🔥 Cost-effective: Avoiding the need for human labels or API calls for general LVLMs throughout the entire pipeline endows Spatial-SSRL with cost-effectiveness.
🔥 Lightweight: Prior approaches for spatial understanding heavily rely on annotation of external tools, incurring inherent errors in training data and additional cost. In constrast, Spatial-SSRL is completely tool-free and can easily be extended to more self-supervised tasks.
🔥 Naturally Verifiable: Intrinsic supervisory signals determined by pretext objectives are naturally verifiable, aligning Spatial-SSRL well with the RLVR paradigm.

📊 Results

We train Qwen3-VL-4B-Instruct with our Spatial-SSRL paradigm and the average experimental results on spatial understanding and general VQA benchmarks are shown below.

Pipeline

🛠️ Usage

Here we provide a code snippet for you to start a simple trial of Spatial-SSRL-Qwen3VL-4B on your own device. You can download the model from 🤗Spatial-SSRL-Qwen3VL-4B Model before your trial!

from transformers import AutoProcessor, AutoModelForImageTextToText #transformers==4.57.1
from qwen_vl_utils import process_vision_info #0.0.14
import torch

model_path = "internlm/Spatial-SSRL-Qwen3VL-4B" #You can change it to your own local path if deployed already

#Change the path of the input image
img_path = "assets/eg1.jpg"

#Change your question here
question = "Question: Consider the real-world 3D locations and orientations of the objects. If I stand at the man's position facing where it is facing, is the menu on the left or right of me?\nOptions:\nA. on the left\nB. on the right\n"

question += "Please select the correct answer from the options above. \n"
#We recommend using the format prompt to make the inference consistent with training
format_prompt = "You FIRST think about the reasoning process as an internal monologue and then provide the final answer. The reasoning process MUST BE enclosed within <think> </think> tags. The final answer MUST BE put in \\boxed{}."

model = AutoModelForImageTextToText.from_pretrained(
    model_path, torch_dtype=torch.float16, device_map='auto', attn_implementation='flash_attention_2'
    )
processor = AutoProcessor.from_pretrained(model_path)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "image",
                "image": img_path,
            },
            {"type": "text", "text": question + format_prompt},
        ],
    }
]

text = processor.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
    text=[text],
    images=image_inputs,
    videos=video_inputs,
    padding=True,
    return_tensors="pt",
)
inputs = inputs.to("cuda")

generated_ids = model.generate(**inputs, max_new_tokens=4096, do_sample=False)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print("Model Response:", output_text[0])

Cases

Teaser

✒️Citation

If you find our model useful, please kindly cite:

@article{liu2025spatial,
  title={Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised Reinforcement Learning},
  author={Liu, Yuhong and Zhang, Beichen and Zang, Yuhang and Cao, Yuhang and Xing, Long and Dong, Xiaoyi and Duan, Haodong and Lin, Dahua and Wang, Jiaqi},
  journal={arXiv preprint arXiv:2510.27606},
  year={2025}
}