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
Download config.json from THUSI-Lab/GameScaling: direct link, hf CLI and curl.
- Browser
- Download file 796 Bytes
-
https://huggingface.co/THUSI-Lab/GameScaling/resolve/main/config.json
- Command line
-
hf download hf://THUSI-Lab/GameScaling/config.json
-
curl -L -o config.json https://huggingface.co/THUSI-Lab/GameScaling/resolve/main/config.json
796 Bytes
| { | |
| "name": "GameScaling", | |
| "description": "Index of the GameScaling checkpoints. This repository holds the inference client, prompts and scripts; the weights live in the checkpoint repositories below.", | |
| "collection": "https://huggingface.co/collections/THUSI-Lab/gamescaling-model-6acf7c0c6500ebee61a7f872", | |
| "checkpoints": { | |
| "GameScaling-0.8B-S1": "THUSI-Lab/GameScaling-0.8B-S1", | |
| "GameScaling-0.8B-S3": "THUSI-Lab/GameScaling-0.8B-S3", | |
| "GameScaling-2B-S1": "THUSI-Lab/GameScaling-2B-S1", | |
| "GameScaling-2B-S3": "THUSI-Lab/GameScaling-2B-S3", | |
| "GameScaling-9B-S1": "THUSI-Lab/GameScaling-9B-S1", | |
| "GameScaling-9B-S3": "THUSI-Lab/GameScaling-9B-S3", | |
| "GameScaling-27B-S1": "THUSI-Lab/GameScaling-27B-S1", | |
| "GameScaling-27B-S3": "THUSI-Lab/GameScaling-27B-S3" | |
| } | |
| } | |