Image-Text-to-Text
Transformers
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
Card: Models badge points to the GameScaling collection
Browse files
README.md
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<a href="https://gamescaling.github.io" style="display:inline-block;margin:2px;"><img src="https://img.shields.io/badge/Project-Page-1f6feb?style=for-the-badge&logo=googlechrome&logoColor=white" alt="Project Page" style="display:inline-block;vertical-align:middle;margin:0;"></a>
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<a href="https://gamescaling.github.io" style="display:inline-block;margin:2px;"><img src="https://img.shields.io/badge/arXiv-coming%20soon-lightgrey?style=for-the-badge&logo=arxiv&logoColor=white" alt="arXiv (coming soon)" style="display:inline-block;vertical-align:middle;margin:0;"></a>
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<a href="https://github.com/THUSI-Lab/GameScaling" style="display:inline-block;margin:2px;"><img src="https://img.shields.io/badge/Code-GitHub-181717?style=for-the-badge&logo=github&logoColor=white" alt="Code" style="display:inline-block;vertical-align:middle;margin:0;"></a>
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<a href="https://huggingface.co/THUSI-Lab/
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<a href="https://www.apache.org/licenses/LICENSE-2.0" style="display:inline-block;margin:2px;"><img src="https://img.shields.io/badge/License-Apache%202.0-2ea44f?style=for-the-badge" alt="License: Apache 2.0" style="display:inline-block;vertical-align:middle;margin:0;"></a>
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</p>
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acts.
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This repository contains the inference client and the standard system-prompt template; each of the
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eight checkpoints is its own repository (below). Together they are everything needed to serve a
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checkpoint and run it in a game; there is no training code. Paper results and analysis are on the [project page](https://gamescaling.github.io).
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## Checkpoints
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<a href="https://gamescaling.github.io" style="display:inline-block;margin:2px;"><img src="https://img.shields.io/badge/Project-Page-1f6feb?style=for-the-badge&logo=googlechrome&logoColor=white" alt="Project Page" style="display:inline-block;vertical-align:middle;margin:0;"></a>
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<a href="https://gamescaling.github.io" style="display:inline-block;margin:2px;"><img src="https://img.shields.io/badge/arXiv-coming%20soon-lightgrey?style=for-the-badge&logo=arxiv&logoColor=white" alt="arXiv (coming soon)" style="display:inline-block;vertical-align:middle;margin:0;"></a>
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<a href="https://github.com/THUSI-Lab/GameScaling" style="display:inline-block;margin:2px;"><img src="https://img.shields.io/badge/Code-GitHub-181717?style=for-the-badge&logo=github&logoColor=white" alt="Code" style="display:inline-block;vertical-align:middle;margin:0;"></a>
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<a href="https://huggingface.co/collections/THUSI-Lab/gamescaling-model-6acf7c0c6500ebee61a7f872" style="display:inline-block;margin:2px;"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Models-GameScaling%20collection-ffcc4d?style=for-the-badge" alt="Models" style="display:inline-block;vertical-align:middle;margin:0;"></a>
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<a href="https://www.apache.org/licenses/LICENSE-2.0" style="display:inline-block;margin:2px;"><img src="https://img.shields.io/badge/License-Apache%202.0-2ea44f?style=for-the-badge" alt="License: Apache 2.0" style="display:inline-block;vertical-align:middle;margin:0;"></a>
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</p>
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acts.
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This repository contains the inference client and the standard system-prompt template; each of the
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eight checkpoints is its own repository (below, and in the [GameScaling collection](https://huggingface.co/collections/THUSI-Lab/gamescaling-model-6acf7c0c6500ebee61a7f872)). Together they are everything needed to serve a
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checkpoint and run it in a game; there is no training code. Paper results and analysis are on the [project page](https://gamescaling.github.io).
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## Checkpoints
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