Instructions to use gitcommit90/SenseNova-Loop-9B-Preview-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gitcommit90/SenseNova-Loop-9B-Preview-NVFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="gitcommit90/SenseNova-Loop-9B-Preview-NVFP4", 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("gitcommit90/SenseNova-Loop-9B-Preview-NVFP4", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gitcommit90/SenseNova-Loop-9B-Preview-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gitcommit90/SenseNova-Loop-9B-Preview-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gitcommit90/SenseNova-Loop-9B-Preview-NVFP4",
		"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/gitcommit90/SenseNova-Loop-9B-Preview-NVFP4

SGLang

How to use gitcommit90/SenseNova-Loop-9B-Preview-NVFP4 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 "gitcommit90/SenseNova-Loop-9B-Preview-NVFP4" \
    --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": "gitcommit90/SenseNova-Loop-9B-Preview-NVFP4",
		"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 "gitcommit90/SenseNova-Loop-9B-Preview-NVFP4" \
        --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": "gitcommit90/SenseNova-Loop-9B-Preview-NVFP4",
		"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 gitcommit90/SenseNova-Loop-9B-Preview-NVFP4 with Docker Model Runner:
```
docker model run hf.co/gitcommit90/SenseNova-Loop-9B-Preview-NVFP4
```

SenseNova-Loop-9B-Preview-NVFP4 / figures /README.md

gitcommit90

Publish runtime v2 optimization, final comparisons and figures (weights unchanged)

8edc128 verified 1 day ago

preview code

Raw History Blame Contribute Delete

1.28 kB

# Reproducible publication figures

House style: [figures4papers Scientific Figure Making](https://github.com/ChenLiu-1996/figures4papers/tree/main/scientific-figure-making).

```bash
python3 -m venv .venv-figures
.venv-figures/bin/pip install -r figures/requirements.txt
.venv-figures/bin/python figures/plot_benchmarks.py
```

No hand-entered benchmark values: performance is read from `evaluation/perf_sglang_*.json`, quality from
`evaluation/compare_vs_ref.json`. The header and captions identify the October 30, 2026 functional baseline.
Performance panels distinguish median per-request decode, time to first token, and aggregate output throughput.
Quality panels show task scores and paired-bootstrap 95% intervals from the original saved comparisons.
These intervals reflect this small item set, not run-to-run throughput variation or full-benchmark accuracy.

The originals are 300-DPI PNG plus vector PDF and SVG. No new GPU benchmarks were run to make these figures.

## Runtime v2 final figures

`python figures/plot_runtime_v2.py` reads only `runtime-v2/runs/final_*/` JSONs.
Original v1 figures remain unchanged. New charts separate final matched runs from screening, and
show the first complete quality runs and paired intervals, including fast-profile regressions.

	# Reproducible publication figures

	House style: [figures4papers Scientific Figure Making](https://github.com/ChenLiu-1996/figures4papers/tree/main/scientific-figure-making).

	```bash
	python3 -m venv .venv-figures
	.venv-figures/bin/pip install -r figures/requirements.txt
	.venv-figures/bin/python figures/plot_benchmarks.py
	```

	No hand-entered benchmark values: performance is read from `evaluation/perf_sglang_*.json`, quality from
	`evaluation/compare_vs_ref.json`. The header and captions identify the October 30, 2026 functional baseline.
	Performance panels distinguish median per-request decode, time to first token, and aggregate output throughput.
	Quality panels show task scores and paired-bootstrap 95% intervals from the original saved comparisons.
	These intervals reflect this small item set, not run-to-run throughput variation or full-benchmark accuracy.

	The originals are 300-DPI PNG plus vector PDF and SVG. No new GPU benchmarks were run to make these figures.

	## Runtime v2 final figures

	`python figures/plot_runtime_v2.py` reads only `runtime-v2/runs/final_*/` JSONs.
	Original v1 figures remain unchanged. New charts separate final matched runs from screening, and
	show the first complete quality runs and paired intervals, including fast-profile regressions.