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 / runtime-v2 /bench /README.md

gitcommit90

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

8edc128 verified about 18 hours ago

preview code

Raw History Blame Contribute Delete

2.4 kB

Reproduce runtime v2 measurements

Run one GPU workload at a time. Historical runs/ contains the original final JSONs, per-item evaluation outputs, screening history and logs; paths inside archived JSONs may refer to the original machine. No trace files or session-local GPU-lock/orchestration wrappers are required for serving.

From the repository/snapshot root, after downloading model files as in the main README:

python3 -m venv .venv-eval
.venv-eval/bin/pip install -r evaluation/harness/requirements.lock
MODEL=/absolute/path/to/SenseNova-Loop-9B-Preview-NVFP4
# Select one runtime, wait for readiness, then measure. Stop it before switching to another.
bash runtime/serve_sglang.sh "$MODEL"                          # frozen v1
# MHC_MODE=exact bash runtime-v2/serve_sglang.sh "$MODEL"      # conservative optimized
# MHC_MODE=fast bash runtime-v2/serve_sglang.sh "$MODEL"       # fastest, different summation order
curl -fsS http://127.0.0.1:30080/v1/models
mkdir -p results
.venv-eval/bin/python evaluation/harness/perf_bench.py --base-url http://127.0.0.1:30080/v1 \
  --model sensenova-loop-9b-nvfp4 --concurrency 1,2,4,8 --num-requests 8 --warmup 2 \
  --input-tokens 1024 --output-tokens 256 --tokenizer "$MODEL/tokenizer.json" --out results/matched.json
.venv-eval/bin/python runtime-v2/bench/screen.py --base-url http://127.0.0.1:30080 \
  --model sensenova-loop-9b-nvfp4 --concurrency 1,4,8 --output-tokens 256 --prompt-kind code \
  --tokenizer "$MODEL/tokenizer.json" --out results/code.json
# Repeat with --prompt-kind doc for ~4K input tokens.
# Build the pinned small evaluation set into evaluation/harness/data/ before running quality tests:
.venv-eval/bin/python evaluation/harness/build_eval_data.py
.venv-eval/bin/python evaluation/harness/run_eval.py --base-url http://127.0.0.1:30080/v1 \
  --model sensenova-loop-9b-nvfp4 --out results/quality --concurrency 16

For the 12-case feature test, supply --image (the recorded image was TextVQA validation image 34730): python runtime-v2/tests/greedy_outputs.py results/features.json --image /path/to/image.jpg. Kernel differential tests run in the pinned SGLang container; see tests/test_mhc_fused.py. All run counts and exact prompts/lengths are in saved JSONs. Code/doc final tests use 4/8/16 requests at c=1/4/8; matched tests use 8 per level. Screening c=16 results are not final matched-sweep numbers.