Instructions to use wchai/SenseNova-U1-8B-SI800K-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wchai/SenseNova-U1-8B-SI800K-SFT with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="wchai/SenseNova-U1-8B-SI800K-SFT", 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)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("wchai/SenseNova-U1-8B-SI800K-SFT", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use wchai/SenseNova-U1-8B-SI800K-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wchai/SenseNova-U1-8B-SI800K-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wchai/SenseNova-U1-8B-SI800K-SFT",
		"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/wchai/SenseNova-U1-8B-SI800K-SFT

SGLang

How to use wchai/SenseNova-U1-8B-SI800K-SFT 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 "wchai/SenseNova-U1-8B-SI800K-SFT" \
    --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": "wchai/SenseNova-U1-8B-SI800K-SFT",
		"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 "wchai/SenseNova-U1-8B-SI800K-SFT" \
        --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": "wchai/SenseNova-U1-8B-SI800K-SFT",
		"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 wchai/SenseNova-U1-8B-SI800K-SFT with Docker Model Runner:
```
docker model run hf.co/wchai/SenseNova-U1-8B-SI800K-SFT
```

SenseNova-U1-8B-SI800K-SFT

sensenova/SenseNova-U1-8B-MoT-SFT further supervised-fine-tuned on SenseNova-SI-800K for spatial understanding.

This is an understanding-focused SFT: only the language/understanding branch is optimized on the spatial corpus. The image-generation branch of the unified NEO-unify architecture is inherited unchanged from the base model (not further trained here).

Details


Base	`sensenova/SenseNova-U1-8B-MoT-SFT` (NEO-unify, `NEOChatModel`)
Data	SenseNova-SI-800K — 832K image+text spatial-intelligence samples (depth / distance / size / relative position / multi-view spatial relations)
Objective	understanding-only SFT (generation branch untouched)
Steps	≈183K (8×H100; lr 2e-4 constant; seq len 20480)
Precision	bfloat16

Built with the SenseNova-U1 training stack; recipe summarized above.

Usage

The model uses custom code (NEOChatModel); load with trust_remote_code=True. See the base model card for inference instructions — they apply unchanged.

License

Apache-2.0, inherited from the base model.

Downloads last month: 18

Safetensors

Model size

18B params

Tensor type

BF16

Model tree for wchai/SenseNova-U1-8B-SI800K-SFT

Base model

sensenova/SenseNova-U1-8B-MoT-SFT

Finetuned

(1)

this model