Instructions to use JingyuanHuang/GUI-RD-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JingyuanHuang/GUI-RD-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="JingyuanHuang/GUI-RD-9B")
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("JingyuanHuang/GUI-RD-9B")
model = AutoModelForMultimodalLM.from_pretrained("JingyuanHuang/GUI-RD-9B")
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 JingyuanHuang/GUI-RD-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JingyuanHuang/GUI-RD-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JingyuanHuang/GUI-RD-9B",
		"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/JingyuanHuang/GUI-RD-9B

SGLang

How to use JingyuanHuang/GUI-RD-9B 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 "JingyuanHuang/GUI-RD-9B" \
    --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": "JingyuanHuang/GUI-RD-9B",
		"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 "JingyuanHuang/GUI-RD-9B" \
        --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": "JingyuanHuang/GUI-RD-9B",
		"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 JingyuanHuang/GUI-RD-9B with Docker Model Runner:
```
docker model run hf.co/JingyuanHuang/GUI-RD-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GUI-RD-9B

This is the GUI-RD checkpoint for Trust the Right Teacher: Quality-Aware Self-Distillation for GUI Grounding.

GUI-RD stands for GUI Reliable Distillation. The checkpoint corresponds to the paper main setting: failed-token gate 0.5, scaling coefficient 3, and teacher top1-probability scaling enabled.

Paper: https://arxiv.org/abs/2606.18101
Hugging Face paper page: https://huggingface.co/papers/2606.18101
Code: https://github.com/HarveyYellow/GUI-RD

Intended Use

This model is intended for GUI grounding research and evaluation. It takes a GUI screenshot and a natural-language instruction, then predicts the target screen coordinate.

Loading

from transformers import AutoModelForMultimodalLM, AutoProcessor
import torch

model_id = "JingyuanHuang/GUI-RD-9B"
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained(
    model_id,
    dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

Depending on your installed Transformers version, the concrete auto-model class for Qwen3.5 may differ. For older Transformers releases, use torch_dtype=torch.bfloat16 instead of dtype=torch.bfloat16. The repository provides standard Transformers config, tokenizer, processor, and safetensors weights.

Citation

@misc{huang2026trustrightteacherqualityaware,
      title={Trust the Right Teacher: Quality-Aware Self-Distillation for GUI Grounding},
      author={Jingyuan Huang and Zuming Huang and Yucheng Shi and Tianze Yang and Xiaoming Zhai and Wei Chu and Ninghao Liu},
      year={2026},
      eprint={2606.18101},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2606.18101},
}