Instructions to use THUSI-Lab/GameScaling-2B-S1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use THUSI-Lab/GameScaling-2B-S1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="THUSI-Lab/GameScaling-2B-S1")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("THUSI-Lab/GameScaling-2B-S1")
model = AutoModelForMultimodalLM.from_pretrained("THUSI-Lab/GameScaling-2B-S1", device_map="auto")
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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use THUSI-Lab/GameScaling-2B-S1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "THUSI-Lab/GameScaling-2B-S1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "THUSI-Lab/GameScaling-2B-S1",
		"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/THUSI-Lab/GameScaling-2B-S1

SGLang

How to use THUSI-Lab/GameScaling-2B-S1 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 "THUSI-Lab/GameScaling-2B-S1" \
    --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": "THUSI-Lab/GameScaling-2B-S1",
		"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 "THUSI-Lab/GameScaling-2B-S1" \
        --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": "THUSI-Lab/GameScaling-2B-S1",
		"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 THUSI-Lab/GameScaling-2B-S1 with Docker Model Runner:
```
docker model run hf.co/THUSI-Lab/GameScaling-2B-S1
```

iTheresaApocalypse commited on 8 days ago

Commit

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Card: aligned with THUSI-Lab/GameScaling

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README.md +6 -6

README.md CHANGED Viewed

@@ -48,12 +48,12 @@ tags:
   <img src="https://huggingface.co/THUSI-Lab/GameScaling/resolve/main/assets/teaser.jpg" alt="From one physical world to diverse virtual worlds" width="100%">
 </p>
-> **This repository: GameScaling-2B-S1.** Qwen3.5-2B continued pre-trained on Minecraft gameplay only
-> (S1), 1,600 h of gameplay, 6,736 training steps; 5.4 GB of bfloat16 weights on
-> 1 GPU. Serve it with
-> `bash scripts/serve_vllm.sh THUSI-Lab/GameScaling-2B-S1 8000` (§1). The inference client, prompts and
-> scripts are in [THUSI-Lab/GameScaling](https://huggingface.co/THUSI-Lab/GameScaling); all eight checkpoints are in the
-> [GameScaling collection](https://huggingface.co/collections/THUSI-Lab/gamescaling-model-6acf7c0c6500ebee61a7f872).
 ## Overview

   <img src="https://huggingface.co/THUSI-Lab/GameScaling/resolve/main/assets/teaser.jpg" alt="From one physical world to diverse virtual worlds" width="100%">
 </p>
+**This repository: GameScaling-2B-S1.** Qwen3.5-2B continued pre-trained on Minecraft gameplay only
+(S1), 1,600 h of gameplay, 6,736 training steps; 5.4 GB of bfloat16 weights on
+1 GPU. Serve it with
+`bash scripts/serve_vllm.sh THUSI-Lab/GameScaling-2B-S1 8000` (§1). The inference client, prompts and
+scripts are in [THUSI-Lab/GameScaling](https://huggingface.co/THUSI-Lab/GameScaling); all eight checkpoints are in the
+[GameScaling collection](https://huggingface.co/collections/THUSI-Lab/gamescaling-model-6acf7c0c6500ebee61a7f872).
 ## Overview