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 8 days ago

Commit

acc95d9

verified ·

1 Parent(s): e801016

Update VLAC-Cut title and positioning

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README.md +7 -8

README.md CHANGED Viewed

@@ -12,23 +12,22 @@ tags:
 license: other
 ---
-# VLAC-Cut
-<p align="center">
-  <strong>Video-based Task Progress Estimation for Robot Manipulation</strong>
-</p>
 <div align="center">
-[Paper](https://arxiv.org/abs/2607.09776) · [Code](https://github.com/InternRobotics/VLAC-cut) · [Model](https://huggingface.co/InternRobotics/VLAC-Cut) · [Benchmark](https://huggingface.co/datasets/InternRobotics/VLAC-Cut-Benchmark)
 </div>
 ## Overview
-**VLAC-Cut** is a video-language model for estimating the temporal progress of robot manipulation tasks. Given a natural-language task instruction, an optional task plan, and an execution video, the model produces timestamped progress estimates throughout the execution.
-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, and subsequent recovery.
 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.

 license: other
 ---
+# VLAC-Cut: Process-Level Robot Trajectory Curation via Video Progress Estimation
 <div align="center">
+[Paper](https://arxiv.org/abs/2607.09776) ·
+[Code](https://github.com/InternRobotics/VLAC-cut) ·
+[Model](https://huggingface.co/InternRobotics/VLAC-Cut) ·
+[Benchmark](https://huggingface.co/datasets/InternRobotics/VLAC-Cut-Benchmark)
 </div>
 ## 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.