Image-Text-to-Text
Transformers
Safetensors
English
gamescaling
game-agent
embodied-agent
keyboard-mouse
continued-pretraining
qwen3_5
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
Model card: larger title, balanced affiliations, full-width logos
Browse files
README.md
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<img src="https://huggingface.co/THUSI-Lab/GameScaling/resolve/main/assets/logos/affiliations.png" alt="miHoYo · Tsinghua University · University of Chinese Academy of Sciences · The University of Hong Kong" width="
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<h1 align="center" style="font-size:3.4em;line-height:1.1;margin:0.2em 0 0.15em;">Scaling in Games</h1>
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<h2 align="center" style="font-size:1.6em;line-height:1.3;font-weight:600;margin:0 0 0.9em;border:none;">Continued Pre-Training for Embodied Agents in Diverse Virtual Worlds</h2>
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<b>Kuan Zhang</b><sup>1,2,*,‡</sup> <b>Yukun Chen</b><sup>1,3,*,‡</sup> <b>Zhihao Yang</b><sup>1,3,*,‡</sup> Yue Su<sup>4</sup><br>
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Xiangnan Wu<sup>3</sup> Zirong Chen<sup>2</sup> Run Luo<sup>1,5,‡</sup> Jinkun Hou<sup>6</sup><br>
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Tao Tan<sup>1</sup> <b>Yinhe Zheng</b><sup>1,†</sup> <b>Yiming Li</b><sup>2,†</sup>
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<sup>1</sup>miHoYo Honkai AI R&D Team · <sup>2</sup>College AI, Tsinghua University<br>
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<sup>3</sup>University of Chinese Academy of Sciences · <sup>4</sup>MMLab, The University of Hong Kong<br>
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<sup>5</sup>National University of Singapore · <sup>6</sup>Peking University
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<sup>*</sup>Equal contribution <sup>†</sup>Corresponding author <sup>‡</sup>Work done while interning at miHoYo
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<img src="https://huggingface.co/THUSI-Lab/GameScaling/resolve/main/assets/logos/affiliations.png" alt="miHoYo · Tsinghua University · University of Chinese Academy of Sciences · The University of Hong Kong" width="100%" style="width:100%;max-width:100%;">
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