Instructions to use Jaew00Lee/HiVis-critic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jaew00Lee/HiVis-critic with Transformers:

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

pipe = pipeline("image-text-to-text", model="Jaew00Lee/HiVis-critic")
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("Jaew00Lee/HiVis-critic")
model = AutoModelForImageTextToText.from_pretrained("Jaew00Lee/HiVis-critic")
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 Jaew00Lee/HiVis-critic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jaew00Lee/HiVis-critic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jaew00Lee/HiVis-critic",
		"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/Jaew00Lee/HiVis-critic

SGLang

How to use Jaew00Lee/HiVis-critic 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 "Jaew00Lee/HiVis-critic" \
    --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": "Jaew00Lee/HiVis-critic",
		"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 "Jaew00Lee/HiVis-critic" \
        --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": "Jaew00Lee/HiVis-critic",
		"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 Jaew00Lee/HiVis-critic with Docker Model Runner:
```
docker model run hf.co/Jaew00Lee/HiVis-critic
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for HiVis-critic for HiVis (History-aware Visually grounded) test-time intervention framework

Model Description

HIVIS (History-aware Visually grounded), a test-time intervention framework designed to equip CUAs with history state tracking and visually grounded error analysis. Inside our framework, we propose HiVis-critic, a multimodal model to serve as an intervention engine with these dual critique generation capabilities.

Key Highlights:

📝 HiVis-critic for history state tracking: maintains a macro-action history, a compact record of past interactions to date, recursively compressing past interactions into multi-step achieved goals, enabling better history-aware planning of policies over long horizons.
🎯 HiVis-critic for visually grounded error analysis: verifies raw execution coordinates against actual visual states. If a proposed action is flawed, the model identifies the error dimension to provide the policy with corrective guidance before execution.

Developed by: Jaewoo Lee, Zaid Khan, Archiki Prasad, Justin Chih-Yao Chen, Supriyo Chakraborty, Kartik Balasubramaniam, Sambit Sahu, Elias Stengel-Eskin, Hyunji Lee, Mohit Bansal
Model type: Qwen3ForCausalLM, fine-tuned Large Language Model
Language(s) (NLP): English
License: MIT
Finetuned from model: Qwen3-VL-8B-Thinking

Model Sources

Repository: https://github.com/G-JWLee/HiVis
Paper: A History-Aware Visually Grounded Critic for Computer Use Agents

Overview of HiVis

Uses

Test-time intervention

The HiVis-critic model serves as an intervention engine with these dual critique generation capabilities to aid policies in long-horizon GUI tasks, enabling precise error analysis before execution and providing a history state tracking that allows better decisions.

Citation

If you find this work useful, please consider citing us:

@article{lee2026hisvis,
      title={A History-Aware Visually Grounded Critic for Computer Use Agents},
      author={Jaewoo Lee and Zaid Khan and Archiki Prasad and Justin Chih-Yao Chen and Supriyo Chakraborty and Kartik Balasubramaniam and Sambit Sahu and Elias Stengel-Eskin and Hyunji Lee and Mohit Bansal},
      year={2026},
      journal={arXiv preprint arXiv:tbd},
      url={https://arxiv.org/abs/tbd},
}

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Safetensors

Model size

9B params

Tensor type

BF16

Model tree for Jaew00Lee/HiVis-critic

Base model

Qwen/Qwen3-VL-8B-Thinking

Finetuned

(62)

this model

Quantizations

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Jaew00Lee
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HiVis-critic