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 files using upload-large-folder tool
Browse files
models/27b_s3_1600h_gs7020/config.json
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"Qwen3_5ForConditionalGeneration"
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],
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"dtype": "
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"eos_token_id": 248046,
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"image_token_id": 248056,
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"model_type": "qwen3_5",
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"attention_dropout": 0.0,
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"attn_output_gate": true,
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"dtype": "
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"full_attention_interval": 4,
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"head_dim": 256,
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"vision_config": {
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"deepstack_visual_indexes": [],
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"depth": 27,
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"dtype": "
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_size": 1152,
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"in_channels": 3,
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"Qwen3_5ForConditionalGeneration"
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],
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"dtype": "bfloat16",
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"eos_token_id": 248046,
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"image_token_id": 248056,
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"model_type": "qwen3_5",
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"attention_dropout": 0.0,
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"attn_output_gate": true,
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"bos_token_id": null,
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"dtype": "bfloat16",
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"eos_token_id": 248044,
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"full_attention_interval": 4,
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"head_dim": 256,
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"vision_config": {
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"deepstack_visual_indexes": [],
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"depth": 27,
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"dtype": "bfloat16",
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_size": 1152,
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"in_channels": 3,
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models/27b_s3_1600h_gs7020/model.safetensors.index.json
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