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
```

futurefantasy commited on 11 days ago

Commit

986931d

verified ·

1 Parent(s): 81cb25c

Update VLAC-Cut citation and model card text

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README.md +13 -6

README.md CHANGED Viewed

@@ -24,9 +24,10 @@ The model is designed for long-horizon robot manipulation videos containing fail
 ## Highlights
-- **Video-level progress understanding:** reasons over temporally ordered robot videos rather than isolated images or image pairs.
 - **Non-monotonic process modeling:** recognizes advancement, stagnation, regression, and recovery.
-- **Generative output:** produces timestamped progress predictions through the standard autoregressive language-model interface, without a task-specific regression head.
 ## Model at a Glance
@@ -35,7 +36,7 @@ The model is designed for long-horizon robot manipulation videos containing fail
 | Base model | `Qwen/Qwen3-VL-30B-A3B-Instruct` |
 | Input | Task instruction, optional task plan, and sampled video frames |
 | Output | Timestamped task progress |
-| Default video sampling rate | `2 Hz` |
 ## Model Design
@@ -105,10 +106,16 @@ python scripts/utils/render_prediction_video.py \
 ## Citation
-Please replace the placeholder below with the final paper BibTeX before public release.
 ```bibtex
 ```
 ## License

 ## Highlights
+- **Video-level progress understanding:** reasons over robot videos rather than isolated images or image pairs, and cuts the video into good segments and bad segments.
 - **Non-monotonic process modeling:** recognizes advancement, stagnation, regression, and recovery.
+- **General understanding capability:** enables zero-shot generalization across tasks, scenes, and viewpoints.
+- **Coarse/Fine-grained flexible:** adjusts the frequency of video understanding, achieving progress analysis from coarse-grained to fine-grained.
 ## Model at a Glance
 | Base model | `Qwen/Qwen3-VL-30B-A3B-Instruct` |
 | Input | Task instruction, optional task plan, and sampled video frames |
 | Output | Timestamped task progress |
+| Flexible video sampling rate | `2 Hz`-`20 Hz` (default: `2.0`) |
 ## Model Design
 ## Citation
 ```bibtex
+@misc{zhai2026maximizinghumanefficiencylargescale,
+      title={Maximizing Human Efficiency in Large-Scale Robot Post-Training via VLAC-Cut Guided Pipeline},
+      author={Shaopeng Zhai and Qi Zhang and Tianyi Zhang and Haoran Zhang and Fuxian Huang and Zhanhui Lin and Zijun Xu},
+      year={2026},
+      eprint={2607.09776},
+      archivePrefix={arXiv},
+      primaryClass={cs.RO},
+      url={https://arxiv.org/abs/2607.09776},
+}
 ```
 ## License