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
Single <think>-prefilled prompt format, no-reasoning mode, full Doom example; drop benchmark presets
893fd86 verified Download gamescaling/__init__.py from THUSI-Lab/GameScaling: direct link, hf CLI and curl.
- Browser
- Download file 948 Bytes
-
https://huggingface.co/THUSI-Lab/GameScaling/resolve/main/gamescaling/__init__.py
- Command line
-
hf download hf://THUSI-Lab/GameScaling/gamescaling/__init__.py
-
curl -L -o __init__.py https://huggingface.co/THUSI-Lab/GameScaling/resolve/main/gamescaling/__init__.py
948 Bytes
| """Inference-side contract of the GameScaling checkpoints: prompt, client, action decoding.""" | |
| from .action import ( | |
| ACTION_END, | |
| ACTION_START, | |
| CHUNK_MS, | |
| CHUNK_STEPS, | |
| KEY_VOCAB, | |
| NOOP_ACTION_TEXT, | |
| STEP_MS, | |
| Action, | |
| parse_action, | |
| split_reasoning, | |
| ) | |
| from .policy import ( | |
| NO_THINK_PREFILL, | |
| THINK_PREFILL, | |
| Decision, | |
| GameScalingPolicy, | |
| PolicyConfig, | |
| build_request, | |
| encode_image, | |
| finalize, | |
| ) | |
| from .prompt import ( | |
| USER_TURN_TEXT, | |
| build_system_prompt, | |
| translate_controls, | |
| ) | |
| __version__ = "0.1.0" | |
| __all__ = [ | |
| "ACTION_END", "ACTION_START", "CHUNK_MS", "CHUNK_STEPS", "KEY_VOCAB", "NOOP_ACTION_TEXT", | |
| "STEP_MS", "Action", "parse_action", "split_reasoning", | |
| "NO_THINK_PREFILL", "THINK_PREFILL", "Decision", "GameScalingPolicy", "PolicyConfig", | |
| "build_request", "encode_image", "finalize", | |
| "USER_TURN_TEXT", "build_system_prompt", "translate_controls", | |
| ] | |