Image-Text-to-Text
Transformers
Safetensors
English
Chinese
sensenova_loop
text-generation
multimodal
looped-transformer
mhc
custom_code
conversational
Instructions to use sensenova/SenseNova-Loop-9B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sensenova/SenseNova-Loop-9B-Preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="sensenova/SenseNova-Loop-9B-Preview", trust_remote_code=True) 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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("sensenova/SenseNova-Loop-9B-Preview", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use sensenova/SenseNova-Loop-9B-Preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "sensenova/SenseNova-Loop-9B-Preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "sensenova/SenseNova-Loop-9B-Preview", "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/sensenova/SenseNova-Loop-9B-Preview
- SGLang
How to use sensenova/SenseNova-Loop-9B-Preview 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 "sensenova/SenseNova-Loop-9B-Preview" \ --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": "sensenova/SenseNova-Loop-9B-Preview", "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 "sensenova/SenseNova-Loop-9B-Preview" \ --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": "sensenova/SenseNova-Loop-9B-Preview", "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 sensenova/SenseNova-Loop-9B-Preview with Docker Model Runner:
docker model run hf.co/sensenova/SenseNova-Loop-9B-Preview
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<title id="title">Loop-9B execution overview</title>
<desc id="desc">Text and image inputs pass through early layers, then through the same layers 4 to 27 twice with two mHC residual streams, then through the remaining layers to generate text.</desc>
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<text x="48" y="79" font-family="Arial, sans-serif" font-size="14" fill="#615174">A repeated block adds computation while reusing its weights</text>
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<text x="108" y="187" font-size="17" font-weight="700" fill="#302447">Input</text>
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<text x="274" y="187" font-size="17" font-weight="700" fill="#4a3573">Early layers</text>
<text x="274" y="211" font-size="13" fill="#5c516d">0–3</text>
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<text x="640" y="134" font-size="15" font-weight="700" fill="#5e478a">SHARED BLOCK · LAYERS 4–27 · mHC 2 STREAMS</text>
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<text x="501" y="194" font-size="17" font-weight="700" fill="#ffffff">Pass 1</text>
<text x="501" y="215" font-size="13" fill="#ede7f8">layers 4–27</text>
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<text x="778" y="194" font-size="17" font-weight="700" fill="#ffffff">Pass 2</text>
<text x="778" y="215" font-size="13" fill="#ede7f8">same weights</text>
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<text x="1002" y="187" font-size="16" font-weight="700" fill="#4a3573">Final layers</text>
<text x="1002" y="211" font-size="13" fill="#5c516d">and head</text>
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<text x="1130" y="187" font-size="15" font-weight="700" fill="#302447">Text</text>
<text x="1130" y="208" font-size="15" font-weight="700" fill="#302447">output</text>
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<text x="48" y="341" font-family="Arial, sans-serif" font-size="13" fill="#877b98">Schematic · Layer indices are zero-based · Not all internal operations are shown</text>
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