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 Jun 22

Commit

c332bdf

verified ·

1 Parent(s): 4d790be

Update model card and quick start docs

Browse files

Files changed (1) hide show

quick_start/README.md +13 -4

quick_start/README.md CHANGED Viewed

@@ -1,12 +1,19 @@
 # Quick Start
 This quick start runs the released VLAC model on one bundled example video with the `chunk_all` prompt.
-The bundled `mp4` is sampled on the fly into a fixed `2hz` frame list, then the predicted progress points are aligned to the benchmark dense GT curve for full-trajectory comparison.
 ## Requirements
-- environment: `swift-vlac311-vllm011`
-- hardware: at least `1` GPU with enough memory for this `30B` VLAC checkpoint
 ## Run
@@ -38,10 +45,12 @@ Each output file contains one JSONL row with:
 - example id
 - task metadata
-- `2hz` sampled input frame indices and timestamps
 - chunk_all prompt text
 - raw model response
 - parsed predicted key points: `时间 / 进度`
 - benchmark dense GT progress and semantic anchors
 - predicted dense curve aligned to the GT timeline
 - full-curve comparison metrics and an optional comparison plot path

 # Quick Start
 This quick start runs the released VLAC model on one bundled example video with the `chunk_all` prompt.
+The bundled `mp4` is sampled on the fly into a fixed `2 Hz` frame list, then the predicted progress points are aligned to the bundled dense GT curve for full-trajectory comparison.
 ## Requirements
+- `python >= 3.10`
+- `torch`
+- `transformers >= 4.57`
+- `ms-swift`
+- `decord`
+- `numpy`
+- `Pillow`
+- `matplotlib` optional, only for automatic curve plots
+- at least `1` GPU with enough memory for this `30B` checkpoint
 ## Run
 - example id
 - task metadata
+- `2 Hz` sampled input frame indices and timestamps
 - chunk_all prompt text
 - raw model response
 - parsed predicted key points: `时间 / 进度`
 - benchmark dense GT progress and semantic anchors
 - predicted dense curve aligned to the GT timeline
 - full-curve comparison metrics and an optional comparison plot path
+If `matplotlib` is unavailable, the JSONL output is still written and only the comparison plot is skipped.