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
Commit History
Yesterday
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Card: one button per checkpoint repository
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Add root config.json (checkpoint index) so downloads of this repo are counted
Verified signatureThe Hub counts model downloads on requests for the root config.json. It lists the eight checkpoint repositories and holds no weights.d2c00b6 -
Card: Models badge points to the GameScaling collection
Verified signature48067fe -
Move the eight checkpoints to their own repos (THUSI-Lab/GameScaling-<size>-<mix>)
Verified signatureThe Hub counts model downloads on the root config.json only, so checkpoints in models/<name>/ never registered a download. Each checkpoint now lives at the root of its own repo; this repo keeps the client, prompts and scripts.e88656b
4 days ago
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Card: research-only licence; Code badge -> GitHub
Verified signature5a81b05
5 days ago
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Model card: one-line subtitle
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Model card: logos on top, Video-Arena style title
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Model card: badges in the Video-Arena style
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Model card: drop the separate Paper badge, keep arXiv
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Model card: fit test-frame blocks to the column
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Add a Doom test frame with the model's reply and an end-to-end example script
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Model card: narrower message block
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Model card: fix table and message block layout
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Single <think>-prefilled prompt format, no-reasoning mode, full Doom example; drop benchmark presets
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Model card: no-wrap names and affiliations, narrower file tree
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Model card: plain title, compact authors, file tree, deployment and evaluation focus
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Model card: compact one-row badges, tighter logo strip, no-wrap affiliations
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Model card: larger title, balanced affiliations, full-width logos
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Model card: single-row affiliation logos, inline badges
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Apache-2.0 license; model card with badges, affiliations, teaser and results
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English model card aligned with gamescaling.github.io
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Add 0.8B and 2B (S1, S3) checkpoints, prompt skeleton
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Default: standard build_system_prompt (reasoning=True) + <think> prefill, any game frame
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Add inference client, generic game-screen prompt, and checkpoint configs
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