Instructions to use sarthak2314/GameAgent-IDM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sarthak2314/GameAgent-IDM with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="sarthak2314/GameAgent-IDM")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("sarthak2314/GameAgent-IDM")
model = AutoModelForMultimodalLM.from_pretrained("sarthak2314/GameAgent-IDM", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sarthak2314/GameAgent-IDM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sarthak2314/GameAgent-IDM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sarthak2314/GameAgent-IDM",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/sarthak2314/GameAgent-IDM

SGLang

How to use sarthak2314/GameAgent-IDM 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 "sarthak2314/GameAgent-IDM" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sarthak2314/GameAgent-IDM",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "sarthak2314/GameAgent-IDM" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sarthak2314/GameAgent-IDM",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use sarthak2314/GameAgent-IDM with Docker Model Runner:
```
docker model run hf.co/sarthak2314/GameAgent-IDM
```

GameAgent-IDM

Game-playing Inverse Dynamics Model for predicting keyboard and mouse actions from gameplay video.

Model details

Property	Value
Base model	OpenGVLab/InternVL3-1B-hf
Parameters	0.9B
Precision	BF16
Size	~1.9 GB
Input	Screen frames (448×448) + keyboard/mouse events with timestamps
Output	Predicted keyboard and mouse events between screen frames
Training data	147 h from 29 PC games (GameAgent-480p-100GB)

Usage

from transformers import AutoModelForImageTextToText, AutoProcessor
import torch

model = AutoModelForImageTextToText.from_pretrained(
    "sarthak2314/GameAgent-IDM",
    dtype=torch.bfloat16,
    device_map="cuda",
    trust_remote_code=True,
)
processor = AutoProcessor.from_pretrained(
    "sarthak2314/GameAgent-IDM",
    trust_remote_code=True,
)

@article{choi2025d2e,
  title={D2E: Scaling Vision-Action Pretraining on Desktop Data for Transfer to Embodied AI},
  author={Choi, Suhwan and Jung, Jaeyoon and Seong, Haebin and Kim, Minchan and
          Kim, Minyeong and Cho, Yongjun and Kim, Yoonshik and Park, Yubeen and
          Yu, Youngjae and Lee, Yunsung},
  journal={arXiv preprint arXiv:2510.05684},
  year={2025}
}