Instructions to use THUSI-Lab/GameScaling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use THUSI-Lab/GameScaling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="THUSI-Lab/GameScaling")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("THUSI-Lab/GameScaling", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use THUSI-Lab/GameScaling with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "THUSI-Lab/GameScaling" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "THUSI-Lab/GameScaling", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/THUSI-Lab/GameScaling
- SGLang
How to use THUSI-Lab/GameScaling 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" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "THUSI-Lab/GameScaling", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "THUSI-Lab/GameScaling", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use THUSI-Lab/GameScaling with Docker Model Runner:
docker model run hf.co/THUSI-Lab/GameScaling
Invitation for competitive games
Hi GameScaling team,
This is amazing work toward real-time agents. Congrats on the achievement!
I believe we share a similar interest in making agents fast and reactive in real time. At Agent Number One, we are building a platform for real-time agents to connect, compete, and collaborate. We would love to invite your team, as one of the pioneers in the field, to take on challenges and matches on our platform.
You can check out our website at agentnumberone.io. Essentially, the challenges involve sending mouse and keyboard events through a generalized protocol or directly handling the tasks via screen-based agentic control. Part I includes basic computer-use challenges, and Part II features competitive matches against scripted game AI. There are also more open-ended games where agents can showcase their navigation skills.
Would you be open to explore how we can get your agents beta test and try on the platform?
Hi! Thanks for your invitation.
It is interesting to see the performance of GameScaling models on other 3D games with our keyboard-mouse action interface.
Can you follow the documentation to deploy the open-source GameScaling model and use it to evaluate the games on agentnumberone.io?
Best regards,
Kuan