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

dcbfddc

verified ·

1 Parent(s): 87894ff

Update README.md

Browse files

Files changed (1) hide show

README.md +5 -4

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
@@ -35,13 +36,13 @@ 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
 VLAC-Cut is fine-tuned from Qwen3-VL-30B-A3B-Instruct and retains its original multimodal generation interface. The model receives a task instruction, an optional task plan, and sampled video frames, then generates task progress at different timestamps as text.
-The training data also include supervision for robot behavior description, failure analysis, and correction planning, helping the model understand realistic robot execution processes.
 This repository contains the model checkpoint and loading assets only. Inference scripts, examples, and VPB evaluation code are maintained in the GitHub repository.

 ## 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:** General understanding capability, enabling zero-shot generalization across tasks, scenes, and viewpoints.
+- **Coarse/Fine-grained flexible:** The ability to adjust 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 |
+| flexiable video sampling rate | `2 Hz~20Hz`(Default: `2.0`) |
 ## Model Design
 VLAC-Cut is fine-tuned from Qwen3-VL-30B-A3B-Instruct and retains its original multimodal generation interface. The model receives a task instruction, an optional task plan, and sampled video frames, then generates task progress at different timestamps as text.
+The training data also includes supervision for robot behavior description, failure analysis, and correction planning, helping the model understand realistic robot execution processes.
 This repository contains the model checkpoint and loading assets only. Inference scripts, examples, and VPB evaluation code are maintained in the GitHub repository.