Instructions to use internlm/Spatial-SSRL-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use internlm/Spatial-SSRL-3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="internlm/Spatial-SSRL-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 AutoModel
model = AutoModel.from_pretrained("internlm/Spatial-SSRL-3B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use internlm/Spatial-SSRL-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/Spatial-SSRL-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": "internlm/Spatial-SSRL-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/internlm/Spatial-SSRL-3B

SGLang

How to use internlm/Spatial-SSRL-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 "internlm/Spatial-SSRL-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": "internlm/Spatial-SSRL-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 "internlm/Spatial-SSRL-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": "internlm/Spatial-SSRL-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 internlm/Spatial-SSRL-3B with Docker Model Runner:
```
docker model run hf.co/internlm/Spatial-SSRL-3B
```

Spatial-SSRL-3B / README.md

yuhangzang

Update README.md

f2b8659 verified 4 months ago

preview code

Raw

History Blame Contribute Delete

7.24 kB

	---
	license: apache-2.0
	datasets:
	- internlm/Spatial-SSRL-81k
	language:
	- en
	base_model:
	- Qwen/Qwen2.5-VL-3B-Instruct
	pipeline_tag: image-text-to-text
	library_name: transformers
	tags:
	- multimodal
	- spatial
	- sptial understanding
	- self-supervised learning
	---


	# Spatial-SSRL-3B

	📖<a href="https://arxiv.org/abs/2510.27606">Paper</a>\| 🏠<a href="https://github.com/InternLM/Spatial-SSRL">Github</a> \|🤗<a href="https://huggingface.co/internlm/Spatial-SSRL-7B">Spatial-SSRL-7B Model</a> \|
	🤗<a href="https://huggingface.co/internlm/Spatial-SSRL-3B">Spatial-SSRL-3B Model</a> \| 🤗<a href="https://huggingface.co/internlm/Spatial-SSRL-Qwen3VL-4B">Spatial-SSRL-Qwen3VL-4B Model</a> \|
	🤗<a href="https://huggingface.co/datasets/internlm/Spatial-SSRL-81k">Spatial-SSRL-81k Dataset</a> \| 📰<a href="https://huggingface.co/papers/2510.27606">Daily Paper</a>

	Spatial-SSRL-3B is a large vision-language model targeting spatial understanding, built on the base of Qwen2.5-VL-3B. 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](https://huggingface.co/internlm/Spatial-SSRL-3B), 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](https://huggingface.co/internlm/Spatial-SSRL-Qwen3VL-4B), initialized from Qwen3-VL-4B-Instruct.
	- 🚀 [2025/11/03] Now you can try out Spatial-SSRL-7B on [🤗Spatial-SSRL Space](https://huggingface.co/spaces/yuhangzang/Spatial-SSRL).
	- 🚀 [2025/11/03] We have released the [🤗Spatial-SSRL-7B Model](https://huggingface.co/internlm/Spatial-SSRL-7B), and [🤗Spatial-SSRL-81k Dataset](https://huggingface.co/datasets/internlm/Spatial-SSRL-81k).
	- 🚀 [2025/11/02] We have released the [🏠Spatial-SSRL Repository](https://github.com/InternLM/Spatial-SSRL).

	## 🌈 Overview
	We are thrilled to introduce <strong>Spatial-SSRL</strong>, 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.
	</p>
	<p style="text-align: center;">
	<img src="assets/teaser_1029final.png" alt="Teaser" width="100%">
	</p>
	Spatial-SSRL is a <strong>lightweight</strong> 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. <strong>And we welcome you to join Spatial-SSRL with effective pretext tasks to further strengthen the capabilities of LVLMs!</strong>

	<p style="text-align: center;">
	<img src="assets/pipeline_1029final.png" alt="Pipeline" width="100%">
	</p>

	## 💡 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.
	<p style="text-align: center;">
	<img src="assets/comparison_1029final.png" alt="Teaser" width="100%">
	</p>

	## 📊 Results
	We train Qwen2.5-VL-3B and Qwen2.5-VL-7B with our Spatial-SSRL paradigm and the experimental results across seven spatial understanding benchmarks are shown below.
	<p style="text-align: center;">
	<img src="assets/exp_result.png" alt="Pipeline" width="100%">
	</p>

	## 🛠️ Usage

	Here we provide a code snippet for you to start a simple trial of <strong>Spatial-SSRL-3B</strong> on your own device. You can download the model from 🤗<a href="https://huggingface.co/internlm/Spatial-SSRL-3B">Spatial-SSRL-3B Model</a > before your trial!
	</p>

	```python
	from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
	from qwen_vl_utils import process_vision_info

	model_path = "internlm/Spatial-SSRL-3B" #You can change it to your own local path if deployed already
	img_path = "examples/eg1.jpg"
	question = "Consider the real-world 3D locations of the objects. Which object has a higher location? A. yellow bear kite B. building"
	#We recommend using the format prompt to make the inference consistent with training
	format_prompt = "\n 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 = Qwen2_5_VLForConditionalGeneration.from_pretrained(
	model_path, torch_dtype="auto", device_map="auto"
	)
	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)
	```


	## ✒️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}
	}
	```

	## 📄 License
	![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg) ![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg)

	Usage and License Notices: The data and code are intended and licensed for research use only.