Instructions to use InternRobotics/VLAC-Cut with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InternRobotics/VLAC-Cut with Transformers:

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

pipe = pipeline("image-text-to-text", model="InternRobotics/VLAC-Cut")
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("InternRobotics/VLAC-Cut")
model = AutoModelForMultimodalLM.from_pretrained("InternRobotics/VLAC-Cut", 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 InternRobotics/VLAC-Cut with vLLM:

Install from pip and serve model

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

SGLang

How to use InternRobotics/VLAC-Cut 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 "InternRobotics/VLAC-Cut" \
    --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": "InternRobotics/VLAC-Cut",
		"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 "InternRobotics/VLAC-Cut" \
        --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": "InternRobotics/VLAC-Cut",
		"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 InternRobotics/VLAC-Cut with Docker Model Runner:
```
docker model run hf.co/InternRobotics/VLAC-Cut
```

VLAC-Cut / README.md

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metadata

library_name: transformers
pipeline_tag: image-text-to-text
base_model: Qwen/Qwen3-VL-30B-A3B-Instruct
tags:
  - robotics
  - embodied-ai
  - video-understanding
  - progress-estimation
  - reward-modeling
  - qwen3-vl
license: other

VLAC-Cut: Video Progress Estimation for Process-Level Robot Rollout Segmentation

Paper · Code · Model · Benchmark

Overview

VLAC-Cut is a process-level multimodal trajectory critic for robot post-training data curation. Given a natural-language task instruction, an optional task plan, and a robot rollout video, VLAC-Cut estimates signed task progress over time and identifies temporal segments associated with task advancement or degradation.

Unlike methods that assume task progress increases monotonically over time, VLAC-Cut models non-monotonic execution dynamics, including advancement, stagnation, regression, and recovery. This formulation supports process-level analysis of partial completion, temporary failure, subsequent recovery, and rollout segmentation for post-training data selection.

This Hugging Face repository contains the VLAC-Cut model weights and loading assets. The official inference examples and evaluation code are maintained in the GitHub repository.

Highlights

Video-level temporal reasoning: Analyzes robot execution videos rather than isolated images or image pairs and identifies temporal segments associated with task advancement or degradation.
Non-monotonic progress estimation: Captures advancement, stagnation, regression, and recovery without imposing a monotonically increasing progress assumption.
Zero-shot generalization: Generalizes across manipulation tasks, scenes, object configurations, and camera viewpoints.
Flexible temporal resolution: Supports configurable video sampling frequencies for both coarse- and fine-grained progress estimation.

Model Overview

Property	Description
Base model	`Qwen/Qwen3-VL-30B-A3B-Instruct`
Input	Task instruction, optional task plan, and sampled video frames
Output	Timestamped task-progress estimates
Sampling rate	`2 Hz`-`20 Hz`
Default sampling rate	`2.0 Hz`

Load with Transformers

from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "InternRobotics/VLAC-Cut"

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    dtype="auto",
    device_map="auto",
)
model.eval()

Quick Start

Run progress inference on a local video using the GitHub code:

git clone https://github.com/InternRobotics/VLAC-cut
cd VLAC-cut

python scripts/run_example.py \
  --model-path InternRobotics/VLAC-Cut \
  --video-path <path-to-video.mp4> \
  --task-instruction "<natural-language task instruction>" \
  --task-plan $'<optional step-by-step task plan>' \
  --output-jsonl <path-to-output.jsonl>

Render a prediction JSONL file as an annotated video:

python scripts/utils/render_prediction_video.py \
  --input-jsonl <path-to-output.jsonl> \
  --output-video <path-to-preview.mp4>

Citation

Please cite the following paper when using VLAC-Cut, the released model, or the Video Progress Benchmark:

@misc{zhai2026helphumanefficientlargescalerobot,
      title={HELP: Human-Efficient Large-Scale Robot Post-Training with Rollout Segmentation}, 
      author={Shaopeng Zhai and Qi Zhang and Tianyi Zhang and Haoran Zhang and Fuxian Huang and Zhanhui Lin and Zijun Xu and Weinan Zhang},
      year={2026},
      eprint={2607.09776},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2607.09776}, 
}

License

The model weights and third-party training data may be subject to additional licenses or terms of use. The source code in the GitHub repository is released under the MIT License.