Instructions to use internlm/Spatial-SSRL-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use internlm/Spatial-SSRL-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="internlm/Spatial-SSRL-7B")
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-7B")
model = AutoModelForImageTextToText.from_pretrained("internlm/Spatial-SSRL-7B")
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-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/Spatial-SSRL-7B"
# 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-7B",
		"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-7B

SGLang

How to use internlm/Spatial-SSRL-7B 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-7B" \
    --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-7B",
		"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-7B" \
        --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-7B",
		"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-7B with Docker Model Runner:
```
docker model run hf.co/internlm/Spatial-SSRL-7B
```

yuhangzang commited on Nov 3, 2025

Commit

b03755a

verified ·

1 Parent(s): fe0ed25

Update README.md

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README.md +3 -18

README.md CHANGED Viewed

@@ -56,7 +56,7 @@ We train Qwen2.5-VL-3B and Qwen2.5-VL-7B with our Spatial-SSRL paradigm and the
   <img src="assets/exp_result.png" alt="Pipeline" width="100%">
 </p>
-## ⭐️ Quick Start
 <!--To directly experience <strong>Spatial-SSRL-7B</strong>, you can try it out on huggingface (link)! -->
 Here we provide a code snippet for you to start a simple trial of <strong>Spatial-SSRL-7B</strong> on your own device. You can download the model from 🤗<a href="https://huggingface.co/internlm/Spatial-SSRL-7B">Spatial-SSRL-7B Model</a > before your trial!
 </p>
@@ -111,27 +111,12 @@ output_text = processor.batch_decode(
 print("Model Response:", output_text)
 ```
-## 🛠️ Evaluation
-Prepare your environment:
-```bash
-git clone https://github.com/InternLM/Spatial-SSRL.git
-conda create -n spatialssrl python==3.10
-conda activate spatialssrl
-cd Spatial-SSRL/evaluation
-pip install -r requirements.txt
-```
-Start your evaluation by referring to the tutorials in <a href="https://github.com/InternLM/Spatial-SSRL/blob/main/evaluation/Eval.md">Eval.md</a >
-## 👨‍💻 Todo
-- [ ] Release the training code.
 ## Cases
 <p style="text-align: center;">
-  <img src="examples/case1.jpg" alt="Teaser" width="100%">
 </p>
 <p style="text-align: center;">
-  <img src="examples/case2.jpg" alt="Teaser" width="100%">
 </p>
 ## ✒️Citation

   <img src="assets/exp_result.png" alt="Pipeline" width="100%">
 </p>
+## 🛠️ Usage
 <!--To directly experience <strong>Spatial-SSRL-7B</strong>, you can try it out on huggingface (link)! -->
 Here we provide a code snippet for you to start a simple trial of <strong>Spatial-SSRL-7B</strong> on your own device. You can download the model from 🤗<a href="https://huggingface.co/internlm/Spatial-SSRL-7B">Spatial-SSRL-7B Model</a > before your trial!
 </p>
 print("Model Response:", output_text)
 ```
 ## Cases
 <p style="text-align: center;">
+  <img src="assets/case1.jpg" alt="Teaser" width="100%">
 </p>
 <p style="text-align: center;">
+  <img src="assets/case2.jpg" alt="Teaser" width="100%">
 </p>
 ## ✒️Citation