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
Add root config.json (checkpoint index) so downloads of this repo are counted
Browse filesThe Hub counts model downloads on requests for the root config.json. It lists the eight checkpoint repositories and holds no weights.
- README.md +1 -0
- config.json +15 -0
README.md
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@@ -96,6 +96,7 @@ THUSI-Lab/GameScaling-<size>-<mixture> one repository per checkpoint
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βββ video_preprocessor_config.json
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THUSI-Lab/GameScaling
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βββ prompts/
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β βββ system_prompt_template.txt standard prompt, 3 slots (Β§2)
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β βββ example_doom_battle1.txt complete Doom prompt (Β§2)
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βββ video_preprocessor_config.json
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THUSI-Lab/GameScaling
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βββ config.json index of the eight checkpoint repositories (no weights)
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βββ prompts/
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β βββ system_prompt_template.txt standard prompt, 3 slots (Β§2)
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β βββ example_doom_battle1.txt complete Doom prompt (Β§2)
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config.json
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{
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"name": "GameScaling",
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"description": "Index of the GameScaling checkpoints. This repository holds the inference client, prompts and scripts; the weights live in the checkpoint repositories below.",
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"collection": "https://huggingface.co/collections/THUSI-Lab/gamescaling-model-6acf7c0c6500ebee61a7f872",
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"checkpoints": {
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"GameScaling-0.8B-S1": "THUSI-Lab/GameScaling-0.8B-S1",
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"GameScaling-0.8B-S3": "THUSI-Lab/GameScaling-0.8B-S3",
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"GameScaling-2B-S1": "THUSI-Lab/GameScaling-2B-S1",
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"GameScaling-2B-S3": "THUSI-Lab/GameScaling-2B-S3",
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"GameScaling-9B-S1": "THUSI-Lab/GameScaling-9B-S1",
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"GameScaling-9B-S3": "THUSI-Lab/GameScaling-9B-S3",
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"GameScaling-27B-S1": "THUSI-Lab/GameScaling-27B-S1",
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"GameScaling-27B-S3": "THUSI-Lab/GameScaling-27B-S3"
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}
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}
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