Instructions to use chakra-labs/GLADOS-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chakra-labs/GLADOS-1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="chakra-labs/GLADOS-1")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("chakra-labs/GLADOS-1")
model = AutoModelForImageTextToText.from_pretrained("chakra-labs/GLADOS-1")
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

vLLM

How to use chakra-labs/GLADOS-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chakra-labs/GLADOS-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chakra-labs/GLADOS-1",
		"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/chakra-labs/GLADOS-1

SGLang

How to use chakra-labs/GLADOS-1 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 "chakra-labs/GLADOS-1" \
    --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": "chakra-labs/GLADOS-1",
		"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 "chakra-labs/GLADOS-1" \
        --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": "chakra-labs/GLADOS-1",
		"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 chakra-labs/GLADOS-1 with Docker Model Runner:
```
docker model run hf.co/chakra-labs/GLADOS-1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GLADOS-1 — UI-TARS-7B-SFT

Model Description

GLADOS-1 is the first computer-use (CUA) model post-trained using collective, crowd-sourced trajectories. Leveraging the enourmous PANGO dataset (with primarily Chrome based interactions), it's purpose is to provide a lense as to what's possible with enormous trajectory sizes in computer use.

It also represents the first open-sourced post-training pipeline for UI-TARS, inspired by the existing Qwen2VL finetuning series.

This model is designed to:

Be compliant. It has been taught to rigorouly follow directions and output action formats compatible with downstream parsers like PyAutoGUI.
Understand web productivity applications. The Pango dataset primarily contains productivity application usage in browser. Consequently in OSWorld results, we observe significantly improved performance on the Chrome task bench.
Have strong intuition on visual grounding. Our experiments are detailed more closely here in our research blog.

📕 Release Blog | 🤗 Code | 🔧 Deployment (via UI-TARS) | 🖥️ Running on your own computer (via UI-TARS Desktop)

Citation

@misc{chakralabs2025glados-1,
  author = {Chakra Labs},
  title = {GLADOS-1},
  url = {https://github.com/Chakra-Network/GLADOS-1},
  year = {2025}
}

Downloads last month: 8

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for chakra-labs/GLADOS-1

Base model

ByteDance-Seed/UI-TARS-7B-SFT

Finetuned

(1)

this model

Quantizations

2 models

Datasets used to train chakra-labs/GLADOS-1

Paper for chakra-labs/GLADOS-1

UI-TARS: Pioneering Automated GUI Interaction with Native Agents

Paper • 2501.12326 • Published Jan 21, 2025 • 64