Image-Text-to-Text
Transformers
Safetensors
qwen3_vl_moe
robotics
embodied-ai
video-understanding
progress-estimation
reward-modeling
qwen3-vl
conversational
Instructions to use InternRobotics/VLAC-Cut with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Add examples/example_02/metadata.json
Browse files
examples/example_02/metadata.json
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{
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"example_id": "example_02",
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"bucket": "test_nonexpert_unseen",
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"global_episode_id": "ARX-data/human-data-1230-5cameras/20251229-songjiaqi/20251228-215800-put-plastic bottle-yellow-box-mix/videos/chunk-000/observation.images.front/episode_000007",
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"main_path": "ARX-data/human-data-1230-5cameras/20251229-songjiaqi/20251228-215800-put-plastic bottle-yellow-box-mix/videos/chunk-000/observation.images.front/episode_000007",
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"task_instruction": "抓取塑料瓶使其放进黄色纸箱里",
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"task_description": "抓取塑料瓶使其放进黄色纸箱里:\n开始移动:0%\n爪夹靠近塑料瓶:20%\n爪夹抓取塑料瓶:40%\n爪夹抓住塑料瓶接近黄色纸箱:60%\n爪夹将塑料瓶放入黄色纸箱中:80%\n爪夹松开,塑料瓶落入黄色纸箱:100%",
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"fps": 30.0,
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"is_expert": false,
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"num_frames": 179,
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"final_gt_progress": 100.0,
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"video_path": "examples/example_02/episode.mp4",
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"benchmark_progress_type": "dense_kinematic_progress",
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"benchmark_dense_progress": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.4, 3.9, 4.3, 4.8, 5.2, 5.6, 6.1, 6.5, 6.9, 7.4, 7.8, 8.2, 8.7, 9.1, 9.6, 10.0, 9.9, 9.9, 9.8, 9.8, 9.7, 9.6, 9.6, 9.5, 9.4, 9.4, 9.3, 9.2, 9.2, 9.1, 9.1, 9.0, 9.1, 9.2, 9.3, 9.4, 9.6, 9.7, 9.8, 9.9, 10.0, 10.1, 10.2, 10.3, 10.4, 10.6, 10.7, 10.8, 10.9, 11.0, 12.5, 13.9, 15.4, 16.8, 18.3, 19.8, 21.2, 22.7, 24.2, 25.6, 27.1, 28.5, 30.0, 30.4, 30.8, 31.3, 31.7, 32.1, 32.5, 32.9, 33.4, 33.8, 34.2, 34.6, 35.0, 35.5, 35.9, 36.3, 36.7, 37.1, 37.5, 38.0, 38.4, 38.8, 39.2, 39.6, 40.1, 40.5, 40.9, 41.3, 41.7, 42.2, 42.6, 43.0, 44.9, 46.9, 48.8, 50.8, 52.7, 54.7, 56.6, 58.6, 60.5, 62.5, 64.4, 66.4, 68.3, 70.3, 72.2, 74.2, 76.1, 78.1, 80.0, 81.2, 82.5, 83.8, 85.0, 86.2, 87.5, 88.8, 90.0, 90.4, 90.8, 91.2, 91.7, 92.1, 92.5, 92.9, 93.3, 93.8, 94.2, 94.6, 95.0, 95.4, 95.8, 96.2, 96.7, 97.1, 97.5, 97.9, 98.3, 98.8, 99.2, 99.6, 100.0],
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"benchmark_semantic_anchors": [{"frame": 0, "progress": 0.0}, {"frame": 33, "progress": 3.0}, {"frame": 49, "progress": 10.0}, {"frame": 65, "progress": 9.0}, {"frame": 83, "progress": 11.0}, {"frame": 96, "progress": 30.0}, {"frame": 127, "progress": 43.0}, {"frame": 146, "progress": 80.0}, {"frame": 154, "progress": 90.0}, {"frame": 178, "progress": 100.0}],
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"benchmark_progress_source": {
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"bucket": "test_nonexpert_unseen",
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"global_episode_id": "ARX-data/human-data-1230-5cameras/20251229-songjiaqi/20251228-215800-put-plastic bottle-yellow-box-mix/videos/chunk-000/observation.images.front/episode_000007"
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}
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}
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