Instructions to use Salesforce/GTA1-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Salesforce/GTA1-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="Salesforce/GTA1-7B")
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("Salesforce/GTA1-7B")
model = AutoModelForMultimodalLM.from_pretrained("Salesforce/GTA1-7B", 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 Salesforce/GTA1-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Salesforce/GTA1-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Salesforce/GTA1-7B",
		"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/Salesforce/GTA1-7B

SGLang

How to use Salesforce/GTA1-7B 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 "Salesforce/GTA1-7B" \
    --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": "Salesforce/GTA1-7B",
		"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 "Salesforce/GTA1-7B" \
        --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": "Salesforce/GTA1-7B",
		"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 Salesforce/GTA1-7B with Docker Model Runner:
```
docker model run hf.co/Salesforce/GTA1-7B
```

HelloKKMe commited on Oct 2, 2025

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@@ -35,10 +35,10 @@ We follow the standard evaluation protocol and benchmark our model on three chal
 | UGround-v1-72B    | 72B      | ✅              |  —                | 34.5              |        —          | —  |
 | Qwen2.5-VL-72B-Instruct | 72B | ✅              |  94.00*                | 53.3              |      —            |        62.2*         |
 | UI-TARS           | 72B      | ✅              | 90.3              | 38.1              |        —          |        —          |
-| OpenCUA           | 7B       | ✅              | 92.3              | 43.9*             |      55.3         |       68.3*        |
 | OpenCUA          | 32B      | ✅              | 93.4              | 55.3              |       59.6         |        70.2*          |
 | GTA1-2507 (Ours)              | 7B       | ✅              | 92.4 <sub>*(∆ +2.7)*</sub>             | 50.1<sub>*(∆ +8.1)*</sub>              |     55.1 <sub>*(∆ +2.3)*</sub>          |        67.7 <sub>*(∆ +3.5)*</sub>          |
-| GTA1  (Ours)          | 7B         |     ✅          |  93.4 <sub>*(∆ +0.1)*</sub>              |   55.5<sub>*(∆ +11.6)*</sub>              |    60.1<sub>*(∆ +4.8)*</sub>           |     68.8<sub>*(∆ +0.5)*</sub>            |
 | GTA1 (Ours)              | 32B      | ✅              | 95.2 <sub>*(∆ +1.8)*</sub>             |        63.6<sub>*(∆ +8.3)*</sub>          | 65.2 <sub>*(∆ +5.6)*</sub>             |        72.2<sub>*(∆ +2.0)*</sub>          |
 > **Note:**

 | UGround-v1-72B    | 72B      | ✅              |  —                | 34.5              |        —          | —  |
 | Qwen2.5-VL-72B-Instruct | 72B | ✅              |  94.00*                | 53.3              |      —            |        62.2*         |
 | UI-TARS           | 72B      | ✅              | 90.3              | 38.1              |        —          |        —          |
+| OpenCUA           | 7B       | ✅              | 92.3              | 50.0             |      55.3         |       68.3*        |
 | OpenCUA          | 32B      | ✅              | 93.4              | 55.3              |       59.6         |        70.2*          |
 | GTA1-2507 (Ours)              | 7B       | ✅              | 92.4 <sub>*(∆ +2.7)*</sub>             | 50.1<sub>*(∆ +8.1)*</sub>              |     55.1 <sub>*(∆ +2.3)*</sub>          |        67.7 <sub>*(∆ +3.5)*</sub>          |
+| GTA1 (Ours)           | 7B         |     ✅          |  93.4 <sub>*(∆ +0.1)*</sub>              |   55.5<sub>*(∆ +5.5)*</sub>             |    60.1<sub>*(∆ +4.8)*</sub>           |     68.8<sub>*(∆ +0.5)*</sub>            |
 | GTA1 (Ours)              | 32B      | ✅              | 95.2 <sub>*(∆ +1.8)*</sub>             |        63.6<sub>*(∆ +8.3)*</sub>          | 65.2 <sub>*(∆ +5.6)*</sub>             |        72.2<sub>*(∆ +2.0)*</sub>          |
 > **Note:**